Risk control method and device, storage medium and electronic equipment

Abstract

The embodiment of the invention discloses a risk control method and device, a storage medium and electronic equipment, and the method comprises the steps: determining a to-be-evaluated target, an area where the to-be-evaluated target is located, and a long period and a short period of data collection, and collecting basic data used for representing the economic environment of the area according to the long period. And according to the short period, collecting fluctuation data used for representing economic fluctuation of the region, according to the basic data, determining a basic risk of the region in the long period, and according to the fluctuation data, determining a fluctuation risk of the region in the short period. And according to the basic risk of the region in the long period, determining the basic risk of the region in each short period falling into the long period, and according to the basic risk and the fluctuation risk of the region in each short period, determining the final risk of the region in each short period, thereby determining the risk of the to-be-evaluated target.

Description

Technical Field

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

[0002] At present, in order to effectively manage and avoid risks associated with various companies or enterprises, many investment institutions will conduct risk identification on these companies and determine the credit qualifications of the target companies or enterprises to provide support for investors' decision-making.

[0003] Among them, there are some special enterprises, such as urban investment companies, whose credit risk and financing capabilities are affected by the economic conditions and market dynamics of the region, that is, these special enterprises are often closely linked to the economic strength of the region they are located in. However, the traditional risk control method has a simple analysis dimension and a low frequency of data collection, which cannot fully capture the risk characteristics of enterprises, especially these special enterprises, and cannot meet the risk control needs.

[0004] Therefore, how to identify the risks of these special enterprises has become an urgent problem to be solved. Summary of the invention

[0005] The embodiments of this specification provide a risk control method and device to partially solve the problems existing in the above-mentioned prior art.

[0006] The embodiments of this specification adopt the following technical solutions:

[0007] This specification provides a risk control method, the method comprising:

[0008] Determine the target to be evaluated, the area where the target to be evaluated is located, and the long and short periods for collecting data;

[0009] According to the long cycle, basic data for characterizing the economic environment of the region are collected, and according to the short cycle, fluctuation data for characterizing the economic fluctuation of the region are collected;

[0010] Determine the basic risk of the region in the long period based on the basic data, and determine the volatility risk of the region in the short period based on the volatility data;

[0011] Determine the basic risk of the region in each short period within the long period according to the basic risk of the region in the long period;

[0012] Determine the final risk of the region in each short period according to the basic risk and volatility risk of the region in each short period;

[0013] The risk of the target to be assessed is determined according to the final risk of the area in each short period.

[0014] Optionally, the basic data includes at least one of resource data, debt data, and regional ranking data corresponding to the region;

[0015] The fluctuation data includes at least one of market data, bond data, and negative public opinion data corresponding to the region.

[0016] Optionally, determining the basic risk of the region in the long period according to the basic data specifically includes:

[0017] When the basic data includes the resource data, determining the resource reserve risk of the region in the long period according to the resource data; determining the basic risk of the region in the long period according to the resource reserve risk; and / or,

[0018] When the basic data includes the debt data, the debt risk of the region in the long period is determined according to the debt data; the basic risk of the region in the long period is determined according to the resource debt risk; and / or,

[0019] When the basic data includes the ranking data, the competitiveness level of the region in the long period is determined according to the regional ranking data; and the basic risk of the region in the long period is determined according to the competitiveness level.

[0020] Optionally, determining the volatility risk of the region within the short period according to the volatility data specifically includes:

[0021] When the fluctuation data includes the market data, determining the consumption risk of the region in the short period according to the market data; determining the fluctuation risk of the region in the short period according to the consumption risk; and / or,

[0022] When the volatility data includes the bond data, determining the credit risk of the region in the short period according to the bond data; determining the volatility risk of the region in the short period according to the credit risk; and / or,

[0023] When the fluctuation data includes the negative public opinion data, the reputation risk of the region in the short period is determined based on the negative public opinion data; and the fluctuation risk of the region in the short period is determined based on the reputation risk.

[0024] Optionally, the method further comprises:

[0025] According to the short period, collecting regional interest rate spread data between the yield of regional bonds and the risk-free yield;

[0026] Based on the regional interest rate spread data, the reference risk of the region in the short period is determined.

[0027] Optionally, determining the risk of the target to be assessed according to the final risk of the region in each short period specifically includes:

[0028] Determining the credibility of the final risk according to the final risk of the area in the short period and the reference risk;

[0029] If the credibility is higher than a preset credibility threshold, the risk of the target to be assessed is determined according to the final risk of the area within the short period.

[0030] Optionally, determining the credibility of the final risk specifically includes:

[0031] Determining a risk curve of the final risk with respect to the short period according to the final risk of the area in the short period;

[0032] Determining a reference curve of the reference risk with respect to the short period according to the reference risk of the area in the short period;

[0033] The credibility of the final risk is determined according to the risk curve and the reference curve.

[0034] Optionally, determining the credibility of the final risk according to the risk curve and the reference curve specifically includes:

[0035] determining a correlation between the risk curve and the reference curve;

[0036] The credibility of the final risk is determined according to the correlation and a preset correlation threshold.

[0037] This specification provides a wind control device, the device comprising:

[0038] A first determination module is used to determine the target to be evaluated, the area where the target to be evaluated is located, and the long period and short period for collecting data;

[0039] A collection module, used to collect basic data for characterizing the economic environment of the region according to the long cycle, and to collect fluctuation data for characterizing the economic fluctuation of the region according to the short cycle;

[0040] A second determination module is used to determine the basic risk of the region in the long period according to the basic data, and to determine the volatility risk of the region in the short period according to the volatility data;

[0041] A third determination module is used to determine the basic risk of the region in each short period falling within the long period according to the basic risk of the region in the long period;

[0042] An integration module, used for determining the final risk of the region in each short period according to the basic risk and volatility risk of the region in each short period;

[0043] The evaluation module is used to determine the risk of the target to be evaluated according to the final risk of the area in each short period.

[0044] This specification provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned risk control method is implemented.

[0045] An electronic device provided in this specification includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned risk control method when executing the program.

[0046] At least one of the above technical solutions adopted in the embodiments of this specification can achieve the following beneficial effects:

[0047] The embodiments of this specification disclose a risk control method, which determines a target to be evaluated, a region where the target to be evaluated is located, and a long period and a short period for collecting data; based on the long period, basic data for characterizing the economic environment of the region are collected; based on the short period, fluctuation data for characterizing the economic fluctuations of the region are collected; based on the basic data, a basic risk of the region in the long period is determined; and based on the fluctuation data, a fluctuation risk of the region in the short period is determined; based on the basic risk of the region in the long period, a basic risk of the region in each short period falling within the long period is determined; based on the basic risk of the region in each short period, a final risk of the region in each short period is determined; and based on the final risk of the region in each short period, the risk of the target to be evaluated is determined.

[0048] Through this method, on the basis of determining the basic risk of the region where the target to be evaluated is located through basic data, the economic fluctuations in the region are obtained in a timely manner and the corresponding volatility risks are updated, which increases the dimension of risk assessment and makes the final assessment result more comprehensive and accurate. In addition, for volatility data with increased short-term fluctuations, data collection is performed according to the determined short period, which increases the frequency of data collection. At the same time, when determining the final risk of each short period, the basic risk is taken into account, thereby achieving high-frequency and accurate updates of the final risk, and achieving effective characterization of the regional risk in terms of time series, which helps to improve the foresight of risk warning and help investors achieve risk determination and credit mining for the target to be evaluated. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The drawings described herein are used to provide a further understanding of this specification and constitute a part of this specification. The illustrative embodiments and descriptions of this specification are used to explain this specification and do not constitute an improper limitation on this specification. In the drawings:

[0050] Figure 1 This is a flow chart of a risk control method in this specification;

[0051] Figure 2 Schematic diagram of risk curve and reference curve corresponding to area X and area Y in this specification;

[0052] Figure 3 Schematic diagram of the risk curve, reference curve, base curve and volatility curve for region Z in this specification;

[0053] Figure 4a It is a scatter plot of the basic risk index and the regional median rate of return in this statement;

[0054] Figure 4b It is a scatter plot of the volatility risk index and the regional median rate of return in this specification;

[0055] Figure 4c It is a scatter plot of the final risk index and the regional median rate of return in this manual;

[0056] Figure 4d It is a scatter plot of the reference risk index and the regional median rate of return in this specification;

[0057] Figure 5 is a schematic diagram of a wind control device in this specification;

[0058] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this specification. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solutions and advantages of this specification more clear, the technical solutions of this specification will be clearly and completely described below in combination with the specific embodiments of this specification and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of this specification, not all of them. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this specification.

[0060] The technical solutions provided by the embodiments of this specification are described in detail below in conjunction with the accompanying drawings.

[0061] At present, in order to effectively manage and avoid risks associated with various companies or enterprises, investment institutions usually take a series of comprehensive measures to conduct risk identification and credit qualification assessment.

[0062] Among them, the credit risk and financing capacity of some companies to be evaluated are affected by the economic conditions and market dynamics of the region, that is, these special enterprises are often closely linked to the economic strength of the region where they are located. For example, urban investment companies are special purpose entities established to carry out urban infrastructure construction and public project investment. They bear the heavy responsibility of financing infrastructure projects. These companies are usually established by superior departments and their related departments through grants, land injections or equity injections, and have independent legal personality. Due to the close relationship between urban investment companies and the regions where they are located, their credit qualifications and financing capabilities are affected by the economy, finance, debt and other aspects of the region where the urban investment companies are located. That is, the regional comprehensive economic strength of the region where the urban investment companies are located largely determines the upper limit of the credit qualifications of urban investment companies.

[0063] When confirming the risks of such special companies that are closely linked to the regional economy, many institutions usually conduct risk assessments on the economic environment of the region where the company is located, so as to determine the investment risks of these companies. Specifically, the traditional assessment method only evaluates the three dimensions of the region's economy, debt and finance, which reflect the long-term economic environment of the region. The analysis dimensions are simple and ignore other factors that have a significant impact on regional refinancing and regional development, such as short-term market fluctuations and negative public opinion. In addition, the frequency of data collection and updating is low, and the quantitative indicators used are usually updated on an annual basis, which cannot capture and reflect the immediate changes in regional risks in a timely manner. Therefore, the traditional risk determination method cannot meet the risk control needs of such special enterprises or companies.

[0064] To this end, the embodiments of this specification provide a risk control method for this type of special company.

[0065] Figure 1 The following is a flow chart of a risk control method in this specification, which may include the following steps:

[0066] S100: Determine a target to be evaluated, an area where the target to be evaluated is located, and a long period and a short period for collecting data.

[0067] Specifically, the target to be evaluated mentioned here refers to an enterprise, institution or company that needs to conduct risk confirmation or credit qualification assessment. More specifically, such enterprises, institutions or companies are closely related to the economy of the region where they are located, such as urban investment companies, etc. In the embodiment of the present application, investors or investment institutions may obtain relevant information and investment risk information of the target to be evaluated through enterprise risk control applications or web pages, after uploading data related to the target to be evaluated, or directly searching the target to be evaluated, to help investment institutions or users make investment decisions or enterprise risk management.

[0068] Accordingly, since the operation and development of the target to be evaluated is closely related to the economic conditions of the region where it is located, these enterprise risk control applications or web pages will determine the region where the target to be evaluated is located after determining the target to be evaluated input by the user, and determine the risk analysis result of the region based on the relevant data of the region uploaded by the user, or based on various types of data of the region collected periodically at historical moments, thereby determining the risk of the target to be evaluated, and finally returning the risk assessment result of the target to be evaluated to the user.

[0069] Of course, the enterprise risk control application or website can also be used to monitor and analyze multiple targets to be evaluated in multiple regions. After determining each target to be evaluated, determine the regions where the targets to be evaluated are located, and then number these regions. For each region corresponding to each evaluation target, collect various types of data corresponding to the region according to the set period for risk analysis, thereby determining the risk analysis results of the targets to be evaluated in the region.

[0070] Furthermore, in order to facilitate users to monitor and analyze multiple targets to be evaluated in multiple regions, an intuitive visual interface can also be provided. For example, a map view can be used to display the risk distribution of different regions, and a chart can be used to display the risk level of each target to be evaluated. For example, bubbles can be used to represent the risk index of the region in the risk index interface diagram of each region where the target to be evaluated is located. The larger the bubble, the lower its risk index and the higher the credit qualification of the region.

[0071] Therefore, in this specification, the execution of determining the target to be evaluated, the area where the target to be evaluated is located, and the long and short periods of data collection can be the terminal devices such as laptops, tablets, etc. connected to the enterprise risk control application or web page background. Of course, it can also be a server. For the sake of ease of description, this specification only takes the server as the execution entity as an example to illustrate the risk control method provided in this specification.

[0072] Among them, the "region" mentioned in the embodiments of this specification generally refers to the geographical scope or administrative area where the enterprise, institution or company is located. This "region" can be a city, a province, an economic zone or a broader geographical area. The scope of the specific region is not limited in the embodiments of this specification.

[0073] Furthermore, since a large amount of data needs to be collected to support analysis when evaluating the investment risk of the target to be evaluated, after the server determines the target to be evaluated and the area where the target to be evaluated is located, it will determine the long and short periods for collecting data based on pre-set rules or based on the characteristics of the target to be evaluated.

[0074] Furthermore, the long cycle mentioned here has a longer time span, such as a quarter, year or longer, while the short cycle has a shorter time span, such as a day, week, month, etc. For example, in an embodiment provided in this specification, the long cycle can be one year, while the short cycle can be one day. It should be noted that the time span of the long cycle is greater than that of the short cycle, and the specific time spans of the long cycle and short cycle of the collected data are not limited in this specification embodiment.

[0075] S102: According to the long cycle, basic data for characterizing the economic environment of the region is collected, and according to the short cycle, fluctuation data for characterizing the economic fluctuation of the region is collected.

[0076] Further, after determining the long cycle and short cycle for collecting data, the server will collect the basic data of the area where the target to be evaluated is located in accordance with the determined long cycle through a predetermined basic data collection path, for example, the general public budget revenue, local debt ratio, deposit scale of financial institutions and other data of the area. If the long cycle is one year, the corresponding basic data will be collected at a fixed time each year. It should be noted that the specific content of the basic data is not limited in the embodiments of this specification.

[0077] These basic data are capable of representing the long-term economic environment of a region, including economic indicators, policy changes, social development and other dimensions over a longer period of time, and are used to reflect the overall trends and structural characteristics of the regional economy.

[0078] Correspondingly, the server will also collect fluctuation data of the area where the target to be evaluated is located according to the determined short cycle and through a predetermined fluctuation data collection method, such as regional consumption data, regional municipal bond yields, and regional municipal investment company debt overdue information. If the short cycle is one day, the corresponding fluctuation data will be collected every day. The specific content of the fluctuation data is not limited in the embodiments of this specification.

[0079] Compared with basic data, volatility data focuses more on reflecting the short-term fluctuations of the regional economy, such as cyclical changes in economic activities, the short-term impact of policy adjustments, fluctuations in market sentiment, etc. These data can reveal the real-time dynamics of the regional economy and provide support for short-term investment decisions and risk management.

[0080] Furthermore, data collection can be carried out through a variety of channels, such as obtaining official statistical data from superior departments or statistical agencies, obtaining corresponding data from industry analysis reports issued by professional institutions or consulting companies, or determining the concentration of negative public opinion of the target to be evaluated based on important news, policy releases, and emergencies closely related to the regional economy, etc. The specific collection methods and methods of basic data and fluctuation data are not limited in the embodiments of this specification. In addition, after obtaining the data, the data can also be cleaned, sorted, and analyzed to extract valuable information for subsequent construction of risk assessment models or credit ratings.

[0081] S104: Determine the basic risk of the region in the long period based on the basic data, and determine the volatility risk of the region in the short period based on the volatility data.

[0082] Furthermore, after collecting the basic data and fluctuation data of the region in accordance with the long-term and short-term cycles, the risk situation of the region where the target to be evaluated is located can be evaluated based on these data. Specifically, the basic data and the fluctuation data are first sorted out, and through big data analysis, the basic risk of the region in the long-term cycle is determined based on the basic data to reflect the long-term stability of the regional economy. In addition, based on the fluctuation risk, the fluctuation risk of the region in the short-term cycle is determined to reflect the short-term instability of the regional economy.

[0083] Specifically, the corresponding basic risk level can be determined based on the data in the basic data and the preset basic threshold, and the corresponding volatility risk level can be determined based on the data in the volatility data and the preset volatility threshold. For example, if the debt ratio in the basic data of the region is less than 80%, the basic risk of the region can be set to low risk.

[0084] Furthermore, a risk assessment model can be established through statistical analysis or machine learning to quantify the impact of various risk factors in the basic data and volatility data on the regional economy, thereby determining the quantified basic risk level or index and the corresponding volatility risk level or index of the volatility data.

[0085] Specifically, by using machine learning and other methods, a risk assessment model can be established. Historical data can be collected, including historical basic data and historical fluctuation data, and features or indicators can be extracted from the historical basic data and historical fluctuation data, including growth rate, volatility, policy change index, market sentiment index, etc. Then, a risk assessment model is established using machine learning algorithms, such as logistic regression, neural network, etc. These models output a basic risk level or basic risk index when the features of the extracted basic data are used as input, and output a fluctuation risk level or fluctuation risk index when the features of the extracted fluctuation data are used as input. In the model training process, a part of the historical data needs to be used as a training set to train the model, and another part of the data needs to be used as a validation set to evaluate the performance of the model. By adjusting the parameters and hyperparameters of the model, the prediction ability of the model can be optimized. After the risk assessment model has been trained and verified and has achieved satisfactory performance, new basic data or fluctuation data can be used as input so that the risk assessment outputs a corresponding risk level or risk index.

[0086] Of course, it is also possible to directly standardize the collected basic data and volatility data through big data analysis, and determine the corresponding basic risk level or index based on the standardized basic data. Correspondingly, determine the corresponding volatility risk level or index based on the standardized volatility data.

[0087] It should be noted that when determining volatility risk and underlying risk, the risk can be described in words, such as "high", "medium", "low", etc., instead of using specific numerical values. The risk can also be quantitatively scored to determine the risk index corresponding to the volatility risk and underlying risk, respectively. The method for determining the underlying risk and volatility risk is not limited in the embodiments of this specification, and the forms of expression of the underlying risk and volatility risk are not limited in the embodiments of this specification.

[0088] S106: Determine the basic risk of the region in each short period within the long period according to the basic risk of the region in the long period.

[0089] Specifically, for each long cycle, each short cycle falling within the long cycle can be determined first, and the basic risk corresponding to the long cycle can be directly used as the basic risk of the region in each short cycle falling within the long cycle. For example, if the long cycle is one year and the short cycle is one day, and the basic risk level of the region in 2019 is high, then the basic risk of the region every day in 2019 is also high.

[0090] Of course, it is also possible to determine the basic risk of the region in each short period falling within the long period according to the preset risk conversion rules and the basic risk of the region in the long period. For example, if the long period is one year and the short period is one day, the basic risk index of the region in 2019 is 80 points. If the volatility risk index of the region in the first half of 2019 is low, the basic risk index of the region for each day in the first half of 2019 can be set to 70 points according to the preset line conversion rules. The method of specifically determining the basic risk of the region in each short period falling within the long period is not limited in the embodiments of this specification.

[0091] S108: Determine the final risk of the region in each short period according to the basic risk and volatility risk of the region in each short period.

[0092] Specifically, after determining the basic risk and volatility risk of the region in each short period, the basic risk and volatility risk can be pre-determined according to historical data or risk management policies, and the corresponding weights can be determined. For example, if it is believed that short-term fluctuations have a greater impact on investment decisions, a higher weight may be given to volatility risk.

[0093] Then, the final risk of the region in each short period is determined according to the basic risk and volatility risk of the region in each short period, and the determined weights. For example, the quantitative score of the basic risk and the quantitative score of the volatility risk of the region in each short period are multiplied by their respective weights, and then added to obtain the final risk score, and the final risk score is divided into different risk levels according to the level of the final risk score. Generally, risk levels can be divided into low risk, medium risk and high risk. If the final risk score is lower than a certain threshold, it can be described as "low risk"; if the score is between two thresholds, it can be described as "medium risk"; if it is higher than a certain threshold, it is described as "high risk". If the risk level is expressed in numerical form, the final risk score itself can be used as a risk index without further classification. The higher the index, the greater the risk.

[0094] In addition, the basic risk level and the volatility risk level can also be compared. If both point to the same risk level, for example, both are "high risk", then the final risk level is consistent with them. If the levels of basic risk and volatility risk are inconsistent, different weights can be assigned to them according to the current market conditions and specific risk management strategies. In periods of economic stability, higher weights are given to basic risks, while in periods of high market volatility, higher weights are given to volatility risks. For example, the basic risk level of the area is "medium risk" and the volatility risk level is "high risk". In the current context of economic stability but high market volatility, the weight of volatility risk is higher, and the final risk can be determined as "high risk". Among them, the specific method for determining the final risk is not specifically limited in the embodiments of this specification.

[0095] S110: Determine the risk of the target to be assessed according to the final risk of the area in each short period.

[0096] Specifically, the final risk of the determined region in each short cycle can be used as the basis for assessing the risk of the target to be assessed, and then the correlation between the target to be assessed and the economic conditions of the region can be determined. Based on the correlation and the final risk of the region in each short cycle, combined with the specific risk factors of the target to be assessed, a comprehensive assessment is conducted to determine the specific risk status of the enterprise, that is, to determine the risk of the target to be assessed.

[0097] Of course, since the credit risk and financing capacity of special enterprises such as urban investment companies are greatly affected by the regional economy, the level of regional risk directly affects the market performance and financial stability of these enterprises, and the final risk can also be directly used as the risk of the target to be evaluated. It should be noted that the method for determining the risk of the target to be evaluated is not limited in the embodiments of this specification.

[0098] based on Figure 1 A risk control method is provided, which obtains the economic fluctuations of the region and updates the corresponding fluctuation risks in a timely manner based on the basic risks of the region where the target to be evaluated is located through basic data, thereby increasing the dimension of risk assessment and making the final assessment result more comprehensive and accurate. In addition, for fluctuation data with increased short-term fluctuations, the frequency of data collection is increased according to the determined short-term period, and the basic risks are taken into account when determining the final risks of each short period, thereby achieving high-frequency and accurate updates of the final risks, and achieving effective characterization of the risks of the region in terms of time series, which helps to improve the foresight of risk warnings and help investors achieve risk determination and credit mining for the target to be evaluated.

[0099] Furthermore, the basic data includes at least one of resource data, debt data, and regional ranking data corresponding to the region, and the fluctuation data includes at least one of market data, bond data, and negative public opinion data corresponding to the region.

[0100] Specifically, in order to characterize the economic environment of the region, the basic data includes at least one of the resource data, debt data, and regional ranking data corresponding to the region. Such data can characterize the long-term economic environment of the region from the three dimensions of resources, debt, and regional ranking of the region.

[0101] The resource data includes the natural resources, human resources and capital resources of the region. The richness and utilization efficiency of these resources can reflect the long-term development potential of the region. Debt data includes regional debt ratio, corporate debt and other forms of debt. The debt level and structure can reflect the economic health and debt repayment capacity of the region. Regional ranking data is the ranking of indicators such as regional economic scale, growth rate, and industrial structure, which is used to measure the competitiveness and status of the region in a larger scope.

[0102] Accordingly, in order to characterize the economic fluctuations in the region, the fluctuation data includes at least one of the market data, bond data, and negative public opinion data corresponding to the region. Among them, market data, such as stock market indexes, regional consumption vitality, etc., reflect the real-time dynamics of the market and investor sentiment. Bond data include bond yields and default rates of the target to be evaluated, which can characterize the market's expectations of regional credit risks. Negative public opinion data can measure the public's views on the regional economy and enterprises by analyzing information from sources such as news reports, social media, and public forums, including the concentration of negative public opinion of the target to be evaluated. These fluctuation data fluctuate greatly in the short term and can capture the dynamic changes in the regional economy in the short term for subsequent risk analysis.

[0103] Of course, the basic data may also include other data that can characterize the regional economic environment, and the fluctuation data may also include other data that can characterize the regional economic fluctuation, and this specification embodiment does not impose any specific limitation.

[0104] Furthermore, based on the basic data, the basic risk of the region in the long period is determined, which may be specifically as follows: when the basic data includes the resource data, the resource reserve risk of the region in the long period is determined based on the resource data; the basic risk of the region in the long period is determined based on the resource reserve risk, and / or, when the basic data includes the debt data, the debt risk of the region in the long period is determined based on the debt data; the basic risk of the region in the long period is determined based on the resource-debt risk, and / or, when the basic data includes the ranking data, the competitiveness level of the region in the long period is determined based on the regional ranking data; the basic risk of the region in the long period is determined based on the competitiveness level.

[0105] When the resource data is included in the basic data, the resource reserve risk of the region in the long term can be determined based on the resource data. Specifically, the risk level or quantitative risk index corresponding to the resource data can also be determined by a predetermined resource reserve risk assessment model or big data analysis to characterize the resource reserve risk. Then, based on the resource reserve risk, the basic risk of the region in the long term is determined.

[0106] Correspondingly, when the basic data includes debt data or regional ranking data, etc., the debt risk of the region in the long period can be determined based on the debt data in a similar manner to determining the resource reserve risk based on resource data, and the competitiveness level of the region in the long period can be determined based on the regional ranking data, thereby determining the basic risk of the region in the long period based on the debt risk or the competitiveness level.

[0107] Furthermore, when the basic data contains more than one type of data, the risks corresponding to each type of data can be determined first, and the weights corresponding to each type of data in the basic data can be determined, and then the basic risk can be determined based on the weights and the risks corresponding to each type of data. For example, if the basic data contains resource data, debt data, and regional ranking data at the same time, the corresponding resource reserve risk, debt risk, and competitiveness level can be determined based on these data according to the above method, and then the basic risk of the region in the long period can be determined based on the resource reserve risk, debt risk, competitiveness level, and the predetermined weights.

[0108] Furthermore, based on the volatility data, the volatility risk of the region in the short period is determined, which can be specifically: when the volatility data includes the market data, the consumption risk of the region in the short period is determined based on the market data; the volatility risk of the region in the short period is determined based on the consumption risk, and / or, when the volatility data includes the bond data, the credit risk of the region in the short period is determined based on the bond data; the volatility risk of the region in the short period is determined based on the credit risk, and / or, when the volatility data includes the negative public opinion data, the reputation risk of the region in the short period is determined based on the negative public opinion data; the volatility risk of the region in the short period is determined based on the reputation risk.

[0109] Specifically, when the volatility data contains market data, the consumption risk of the region in the short term can be evaluated by analyzing market data, such as the consumption growth rate, consumer confidence index, etc. If the volatility data contains bond data, such as municipal investment bond yields, bond ratings, bond default rates, etc., the credit risk of the region in the short term can be evaluated by analyzing these data. When the volatility data contains negative public opinion data, the reputation level of the region in the short term can be evaluated by analyzing these public opinions, such as the concentration of negative events of municipal investment companies. Then, based on the consumption risk, credit risk and reputation level, the volatility risk is determined. The specific determination method of the volatility method is similar to the determination of the above-mentioned basic risk, which will not be repeated here.

[0110] Furthermore, in addition to determining the final risk through the volatility risk and basic risk of the region in the short cycle, regional spread data between the yield of the regional bonds and the risk-free rate can be collected according to the short cycle; based on the regional spread data, the reference risk of the region in the short cycle is determined.

[0111] Specifically, the difference between the yield of the regional bonds and the risk-free rate of return, i.e., the regional spread data, can be collected regularly according to the short cycle. Changes in regional spreads usually reflect the market's views and expectations on regional credit risks. If the regional spread rises, it indicates that the market's concerns about regional credit risks have increased; conversely, if the regional spread falls, it indicates that the market's confidence in regional credit has increased. Therefore, the reference risk of the region in the short cycle can be determined based on the changing trend and fluctuation of the regional spread data.

[0112] Specifically, similar to the above-mentioned risk assessment method, the regional interest rate spread data can be quantitatively analyzed or a risk assessment model can be constructed. By constructing the model, the regional interest rate spread data can be converted into a specific risk score or risk index, so as to more intuitively understand the reference risk level of the region.

[0113] Since the basic data and volatility data that determine the final risk have no direct connection with the regional interest rate spread data, but both can reflect the risk changes in the market economic environment, based on this, the reference risk determined according to the regional interest rate spread data can be used as a reference for the final risk to verify the credibility of the final risk.

[0114] Furthermore, the risk of the target to be evaluated is determined based on the final risk of the area in each short period. Specifically, the credibility of the final risk is determined based on the final risk of the area in the short period and the reference risk. If the credibility is higher than a preset credibility threshold, the risk of the target to be evaluated is determined based on the final risk of the area in the short period.

[0115] Specifically, the final risk and the reference risk of the area in the short period can be determined first, and the final risk and the reference risk can be compared to observe the similarities or differences between them. If the two are relatively close or show similar trends of change, then this may indicate that the final risk has a higher credibility.

[0116] In order to determine whether the final risk is credible, a preset credibility threshold can be determined based on historical experience, industry standards or expert opinions, and then the credibility of the calculated final risk is compared with the preset credibility threshold. If the credibility is higher than the threshold, the final risk can be considered reliable and the risk of the target to be assessed can be determined based on it. If the credibility of the final risk is lower than the threshold, further analysis and assessment will be conducted, including re-collecting data, improving assessment methods, and considering other risk factors.

[0117] Among them, determining the credibility of the final risk can be specifically as follows: determining a risk curve of the final risk with respect to the short period according to the final risk of the area in the short period, determining a reference curve of the reference risk with respect to the short period according to the reference risk of the area in the short period, and determining the credibility of the final risk according to the risk curve and the reference curve.

[0118] First, based on the final risk data of the region in multiple short periods, a risk curve of the final risk changing over time can be drawn. This curve shows the fluctuation of the final risk in a short period and reflects the dynamic changes in the regional risk level. Next, based on the reference risk of the region in the same short period, a reference curve of the reference risk changing over time is drawn. The reference curve provides another perspective to observe the changing trend of regional risk and can be used as a comparison and reference for the final risk.

[0119] Then, based on the risk curve and the reference curve, the risk curve of the final risk can be compared with the reference curve of the reference risk to observe the similarities and differences between the two, so as to determine the credibility of the final risk. If the two curves are consistent in trend or show similar fluctuation patterns, then it can be considered that the final risk has a higher credibility. On the contrary, if there are significant differences or contradictions between the two curves, then it may be necessary to further analyze the reasons and consider adjusting the assessment results of the final risk.

[0120] For example, Figure 2 As shown, Figure 2 It includes the risk curve a and reference curve b of region X where the target A to be evaluated is located, and the risk curve c and reference curve d of region Y where the target C to be evaluated is located. By comparing the risk curves of region X and region Y horizontally, it can be seen that the risk index of region X is higher than that of region Y, and the investment risk of target A to be evaluated is greater than that of target C to be evaluated. And if the risk curve of region X and the reference curve, as well as the risk curve of region Y and the reference curve are consistent in trend or show similar fluctuation patterns, the credibility of the final risks of the two regions is high.

[0121] Specifically, determining the credibility of the final risk according to the risk curve and the reference curve may specifically include determining the correlation between the risk curve and the reference curve, and determining the credibility of the final risk according to the correlation and a preset correlation threshold.

[0122] In other words, the credibility of the final risk can be quantitatively determined based on the comparison results of the risk curve and the reference curve. Specifically, the credibility of the final risk can be determined by calculating the correlation coefficient, fit and other statistical indicators of the two curves. If the correlation coefficient is high or the fit is good, the credibility of the final risk is high; conversely, if the correlation coefficient is low or the fit is poor, it is necessary to further analyze the reasons and adjust the assessment results.

[0123] Furthermore, in addition to drawing the risk curve and the reference curve, a basic curve of the determined basic risk with respect to the short period and a fluctuation curve of the determined fluctuation curve with respect to the short period can also be drawn, which are used to further analyze the influencing factors causing the changes when the final risk changes, so as to facilitate the analysis of whether the change in the final risk is caused by the change in the basic data or the change in the fluctuation data.

[0124] For example, Figure 3 As shown, Figure 3 It contains the risk curve, reference curve, basic curve and fluctuation curve of the area Z where the target E to be evaluated is located. The horizontal axis is time and the vertical axis is the quantitative risk index. In this figure, the higher the risk index, the lower the investment risk. Figure 3, we can explain the change of the risk curve of the region Z. Specifically, taking the rapid decline of the risk curve in May 2021 as an example, the main reason is the decline of the basic curve, which may be due to the deterioration of the overall economic environment in the region; since September 2023, benefiting from the package of debt, the risk curve has risen rapidly, and the regional risk has been alleviated. At the same time, it is observed that the basic curve has not changed during this period, but the corresponding rise of the volatility curve is large, reflecting that investors' allocation sentiment for the targets to be evaluated in the region has greatly improved, and even overheating has occurred, which may provide investment opportunities.

[0125] Furthermore, the embodiments of this specification provide a scatter plot of the regional risk index and the regional bond valuation yield, that is, the median regional yield, such as Figure 4a , Figure 4b , Figure 4c , Figure 4d As shown. Among them, the higher the risk index, the lower the investment risk; the higher the regional median rate of return, the lower the investment risk. According to the above relationship and the scatter plot, it can be determined that Figure 4a The correlation coefficient between the basic risk index and the regional median return is -39.70%. Figure 4b The correlation coefficient between the volatility risk index and the regional median return is -73.38%. Figure 4c The correlation coefficient between the final risk index and the regional median rate of return is -54.98%. Figure 4b The correlation coefficient between the reference risk index and the regional median rate of return is -79.76%. As can be seen from the figure, the basic risk index, volatility risk index, final risk index and reference risk index are all significantly negatively correlated with the regional median rate of return. Based on this, it can be seen that the final risk is supported by the basic risk corresponding to the basic data, and the volatility risk corresponding to the volatility data is added. It is sensitive to market heat, can smooth the impact of market sentiment on the final risk of the region, and more accurately reflects the risk fluctuations of the region, which plays an effective role in risk management and investment opportunity mining of the target to be evaluated.

[0126] The above is a risk control method provided in an embodiment of this specification. Based on the same idea, this specification also provides corresponding devices, storage media and electronic devices.

[0127] Figure 5 A schematic diagram of the structure of a wind control device provided in an embodiment of this specification, the device includes:

[0128] A first determination module 600 is used to determine the target to be evaluated, the area where the target to be evaluated is located, and the long period and short period for collecting data;

[0129] A collection module 602 is used to collect basic data for characterizing the economic environment of the region according to the long cycle, and to collect fluctuation data for characterizing the economic fluctuation of the region according to the short cycle;

[0130] A second determination module 604 is used to determine the basic risk of the region in the long period according to the basic data, and to determine the volatility risk of the region in the short period according to the volatility data;

[0131] A third determination module 606 is used to determine the basic risk of the region in each short period falling within the long period according to the basic risk of the region in the long period;

[0132] An integration module 608 is used to determine the final risk of the region in each short period according to the basic risk and volatility risk of the region in each short period;

[0133] The evaluation module 610 is used to determine the risk of the target to be evaluated according to the final risk of the area in each short period.

[0134] Optionally, the basic data includes at least one of resource data, debt data, and regional ranking data corresponding to the region; the fluctuation data includes at least one of market data, bond data, and negative public opinion data corresponding to the region.

[0135] Optionally, the second determination module 604 is specifically used to, when the basic data includes the resource data, determine the resource reserve risk of the region in the long period according to the resource data; determine the basic risk of the region in the long period according to the resource reserve risk; and / or, when the basic data includes the debt data, determine the debt risk of the region in the long period according to the debt data; determine the basic risk of the region in the long period according to the resource debt risk; and / or, when the basic data includes the ranking data, determine the competitiveness level of the region in the long period according to the regional ranking data; determine the basic risk of the region in the long period according to the competitiveness level.

[0136] Optionally, the second determination module 604 is specifically used to, when the fluctuation data includes the market data, determine the consumption risk of the region in the short period according to the market data; determine the fluctuation risk of the region in the short period according to the consumption risk; and / or, when the fluctuation data includes the bond data, determine the credit risk of the region in the short period according to the bond data; determine the fluctuation risk of the region in the short period according to the credit risk; and / or, when the fluctuation data includes the negative public opinion data, determine the reputation risk of the region in the short period according to the negative public opinion data; determine the fluctuation risk of the region in the short period according to the reputation risk.

[0137] Optionally, the integration module 608 is further used to collect regional spread data between the yield of the regional bonds and the risk-free rate according to the short cycle; and determine the reference risk of the region in the short cycle based on the regional spread data.

[0138] Optionally, the integration module 608 is also used to determine the credibility of the final risk based on the final risk of the area within the short period and the reference risk; if the credibility is higher than a preset credibility threshold, the risk of the target to be assessed is determined based on the final risk of the area within the short period.

[0139] Optionally, the integration module 608 is also used to determine a risk curve of the final risk with respect to the short period based on the final risk of the area in the short period; determine a reference curve of the reference risk with respect to the short period based on the reference risk of the area in the short period; and determine the credibility of the final risk based on the risk curve and the reference curve.

[0140] Optionally, the integration module 608 is further configured to determine the correlation between the risk curve and the reference curve; and determine the credibility of the final risk according to the correlation and a preset correlation threshold.

[0141] This specification also provides a computer-readable storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, it can be used to perform the above Figure 1 Provided risk control methods.

[0142] based on Figure 1 Based on the Figure 1 The wind control method shown in the specification also provides Figure 6 The structural diagram of the electronic device shown in FIG. Figure 6At the hardware level, the electronic device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory, and may also include other hardware required for the business. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to achieve the above Figure 1 The risk control method described.

[0143] Of course, in addition to software implementation, this specification does not exclude other implementation methods, such as logic devices or a combination of software and hardware, etc., that is to say, the executor of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0144] In the 1990s, improvements to a technology could be clearly distinguished as hardware improvements (for example, improvements to the circuit structure of diodes, transistors, switches, etc.) or software improvements (improvements to the method flow). However, with the development of technology, many improvements to the method flow today can be regarded as direct improvements to the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved method flow into the hardware circuit. Therefore, it cannot be said that an improvement in a method flow cannot be implemented using a hardware entity module. For example, a programmable logic device (PLD) (such as a field programmable gate array (FPGA)) is such an integrated circuit whose logical function is determined by the user's programming of the device. Designers can "integrate" a digital system on a PLD by programming it themselves, without having to ask a chip manufacturer to design and produce a dedicated integrated circuit chip. Moreover, nowadays, instead of manually making integrated circuit chips, this kind of programming is mostly implemented by "logic compiler" software, which is similar to the software compiler used when developing and writing programs, and the original code before compilation must also be written in a specific programming language, which is called hardware description language (HDL). There is not only one HDL, but many kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. The most commonly used ones are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also know that it is only necessary to program the method flow slightly in the above-mentioned hardware description languages ​​and program it into the integrated circuit, and then it is easy to obtain the hardware circuit that implements the logic method flow.

[0145] The controller can be implemented in any appropriate manner, for example, the controller can take the form of a microprocessor or processor and a computer-readable medium storing a computer-readable program code (such as software or firmware) that can be executed by the (micro)processor, a logic gate, a switch, an application-specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320. The memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art also know that in addition to implementing the controller in a purely computer-readable program code manner, the controller can be implemented in the form of a logic gate, a switch, an application-specific integrated circuit, a programmable logic controller, and an embedded microcontroller by logically programming the method steps. Therefore, this controller can be considered as a hardware component, and the devices included therein for implementing various functions can also be regarded as structures within the hardware component. Or even, the devices for implementing various functions can be regarded as both software modules for implementing the method and structures within the hardware component.

[0146] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0147] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0148] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, this specification may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0149] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0150] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0151] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0152] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0153] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0154] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0155] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0156] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems or computer program products. Therefore, this specification may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0157] This specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0158] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0159] The above description is only an embodiment of the present specification and is not intended to limit the present specification. For those skilled in the art, the present specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present specification shall be included in the scope of the claims of the present specification.

Claims

1. A risk control method, the method comprising: Determine the target to be evaluated, the area where the target to be evaluated is located, and the long and short periods for collecting data; According to the long cycle, basic data for characterizing the economic environment of the region are collected, and according to the short cycle, fluctuation data for characterizing the economic fluctuation of the region are collected; Determine the basic risk of the region in the long period based on the basic data, and determine the volatility risk of the region in the short period based on the volatility data; Determine the basic risk of the region in each short period within the long period according to the basic risk of the region in the long period; Determine the final risk of the region in each short period according to the basic risk and volatility risk of the region in each short period; The risk of the target to be assessed is determined according to the final risk of the area in each short period.

2. The method according to claim 1, wherein the basic data includes at least one of resource data, debt data, and regional ranking data corresponding to the region; The fluctuation data includes at least one of market data, bond data, and negative public opinion data corresponding to the region.

3. The method according to claim 2, determining the basic risk of the region in the long period according to the basic data, specifically comprising: When the basic data includes the resource data, determining the resource reserve risk of the region in the long period according to the resource data; Determine the basic risk of the region in the long period based on the resource reserve risk; and / or, When the basic data includes the debt data, the debt risk of the region in the long period is determined according to the debt data; the basic risk of the region in the long period is determined according to the resource debt risk; and / or, When the basic data includes the ranking data, determining the competitiveness level of the region in the long period according to the region ranking data; Based on the competitiveness level, the basic risk of the region over the long period is determined.

4. The method according to claim 2, determining the volatility risk of the region in the short period according to the volatility data, specifically comprising: When the fluctuation data includes the market data, determining the consumption risk of the region in the short period according to the market data; Determining the volatility risk of the region within the short period based on the consumption risk; and / or, When the volatility data includes the bond data, determining the credit risk of the region in the short period according to the bond data; determining the volatility risk of the region in the short period according to the credit risk; and / or, When the fluctuation data includes the negative public opinion data, the reputation risk of the region in the short period is determined based on the negative public opinion data; and the fluctuation risk of the region in the short period is determined based on the reputation risk.

5. The method of claim 1, further comprising: According to the short period, collecting regional interest rate spread data between the yield of regional bonds and the risk-free yield; Based on the regional interest rate spread data, the reference risk of the region in the short period is determined.

6. The method according to claim 5, determining the risk of the target to be assessed according to the final risk of the region in each short period, specifically comprises: Determining the credibility of the final risk according to the final risk of the area in the short period and the reference risk; If the credibility is higher than a preset credibility threshold, the risk of the target to be assessed is determined according to the final risk of the area within the short period.

7. The method of claim 6, wherein determining the credibility of the final risk comprises: Determining a risk curve of the final risk with respect to the short period according to the final risk of the area in the short period; Determining a reference curve of the reference risk with respect to the short period according to the reference risk of the area in the short period; The credibility of the final risk is determined according to the risk curve and the reference curve.

8. The method according to claim 7, determining the credibility of the final risk according to the risk curve and the reference curve, specifically comprising: determining a correlation between the risk curve and the reference curve; The credibility of the final risk is determined according to the correlation and a preset correlation threshold.

9. A wind control device, comprising: A first determination module is used to determine the target to be evaluated, the area where the target to be evaluated is located, and the long period and short period for collecting data; A collection module, used to collect basic data for characterizing the economic environment of the region according to the long cycle, and to collect fluctuation data for characterizing the economic fluctuation of the region according to the short cycle; A second determination module is used to determine the basic risk of the region in the long period according to the basic data, and to determine the volatility risk of the region in the short period according to the volatility data; A third determination module is used to determine the basic risk of the region in each short period falling within the long period according to the basic risk of the region in the long period; An integration module, used for determining the final risk of the region in each short period according to the basic risk and volatility risk of the region in each short period; The evaluation module is used to determine the risk of the target to be evaluated according to the final risk of the area in each short period.

10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the method according to any one of claims 1 to 8.

11. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 8 when executing the program.

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