Financial risk identification method and system based on big data

Abstract

The invention relates to the technical field of information technology and big data analysis, and discloses a financial risk identification method and system based on big data, a data source module, a risk analysis module, an early warning module and a management response module are established, the data source module is divided into a financial data unit and a non-financial data unit, the risk analysis module is divided into a risk modeling unit, a real-time monitoring adjustment unit and a risk assessment unit and calculates a financial risk identification index Za, a real-time monitoring adjustment coefficient Zl and a comprehensive data security assessment score Zo, the early warning module is divided into an early warning prompt unit and an early warning emergency processing unit, and the early warning module is connected with the management response module through a network. And the management response module adjusts the transmission frequency of the real-time monitoring equipment according to the size of the real-time monitoring adjustment coefficient Zl, and adds safety protection walls for the financial data unit, the non-financial data unit and the network transmission process according to the comprehensive data safety evaluation score Zo.

Description

Technical Field

[0001] The present invention relates to the technical fields of information technology and big data analysis technology, and specifically provides a financial risk identification method and system based on big data. Background Art

[0002] In traditional financial risk management practices, enterprises mainly rely on experience and intuition for risk prevention and usually adopt manual supervision to deal with it. This approach appears to be ineffective when dealing with today's increasingly complex economic operations and huge accounting data. With the advent of the big data era, the development and application of data resources have gradually become a new driving force for enterprise development. Against this background, financial risk management urgently needs to combine big data analysis technology to achieve accurate prediction and effective prevention and control of financial risks. However, there is a significant defect in the existing technologies, that is, the identification and evaluation of potential risks in business processes are insufficient. Most financial risk management systems overly focus on the analysis of financial statements and financial indicators, ignoring the risk points in specific business processes, which limits the comprehensiveness and depth of risk management. Summary of the Invention

[0003] (1) Technical Problems to be Solved

[0004] Aiming at the deficiencies of the existing technologies, the present invention provides a financial risk identification method and system based on big data, which has the advantages of sufficient and detailed identification and evaluation of potential risks in business processes, and solves the problem of insufficient identification and evaluation of potential risks in business processes in the existing technologies.

[0005] (2) Technical Solutions

[0006] To achieve the above object, the present invention provides the following technical solutions: A financial risk identification method based on big data, comprising the following steps:

[0007] Step 1: Establish a data source module, a risk analysis module, an early warning module, and a management response module;

[0008] Step 2: The data source module is divided into a financial data unit and a non-financial data unit;

[0009] Step 3: The risk analysis module is divided into a risk modeling unit, a real-time monitoring and adjustment unit, and a risk assessment unit, and calculates a financial risk identification index Za, a real-time monitoring and adjustment coefficient Zl, and a comprehensive data security assessment score Zo;

[0010] Step 4: The early warning module is divided into an early warning prompt unit and an early warning emergency handling unit, and the early warning module is connected to the management response module through a network;

[0011] Step 5: The management response module adjusts the transmission frequency of the real-time monitoring device according to the size of the real-time monitoring adjustment coefficient Zl, and adds a security firewall to the financial data unit, non-financial data unit, and network transmission process according to the comprehensive data security assessment score Zo.

[0012] Preferably, a financial risk identification system based on big data includes a data source module, a risk analysis module, an early warning module, and a management response module;

[0013] The data source module includes a financial data unit and a non-financial data unit. The financial data unit collects financial data through financial statements and external data sources. The non-financial data unit collects non-financial data through third-party data services, social media, and news. The financial data unit and the non-financial data unit are connected to the risk analysis module through a network;

[0014] The risk analysis module includes a risk modeling unit, a real-time monitoring adjustment unit, and a risk assessment unit. The risk modeling unit calculates the financial risk identification index Za based on financial data. The real-time monitoring adjustment unit calculates the real-time monitoring adjustment coefficient Zl based on financial data and non-financial data. The risk assessment unit calculates the comprehensive data security assessment score Zo based on the financial risk identification index Za and non-financial data. The risk modeling unit, the real-time monitoring adjustment unit, and the risk assessment unit are connected to the early warning module through a network;

[0015] The early warning module includes an early warning prompt unit and an early warning emergency handling unit. The early warning module is connected to the management response module through a network.

[0016] Preferably, the financial data unit numbers the sales revenue, working capital, retained earnings, earnings before interest and taxes, total market value of shareholders' equity, total liabilities, and total assets according to the financial data characteristics. The sales revenue, working capital, retained earnings, earnings before interest and taxes, total market value of shareholders' equity, total liabilities, and total assets are numbered as W 1 、W 2 、W 3 、W 4 、W 5 、F、Q.

[0017] Preferably, the risk modeling unit calculates the financial risk identification index Za based on financial data, and its calculation formula is:

[0018]

[0019] In the formula, Za represents the financial risk identification index, W 1 、W 2 、W 3 、W 4 、W5 F, Q respectively represent sales revenue, working capital, retained earnings, earnings before interest and taxes, total market value of shareholders' equity, total liabilities, and total assets. represents the total asset turnover ratio. represents the liquidity of the company's assets. represents the cumulative profitability of the company. represents the ability of the company to make a profit using all its assets. represents the financial structure, a 1 、a 2 、a 3 、a 4 、a 5 respectively represent the weighted coefficients of the total asset turnover ratio, the liquidity of the company's assets, the cumulative profitability of the company, the ability of the company to make a profit using all its assets, and the financial structure in the financial risk identification index.

[0020] Preferably, the non - financial data unit numbers the number of very satisfied customers, satisfied customers, average customers, dissatisfied customers, very dissatisfied customers, total customers, salary treatment satisfaction, work environment satisfaction, career development opportunity satisfaction, leadership management satisfaction, team cooperation atmosphere satisfaction, R & D investment ratio, number of patents, new product revenue ratio, and innovation culture atmosphere according to non - financial data characteristics. The number of very satisfied customers, satisfied customers, average customers, dissatisfied customers, very dissatisfied customers, and total customers are numbered as m 1 、m 2 、m 3 、m 4 、m 5 、m 6 respectively, and the salary treatment satisfaction, work environment satisfaction, career development opportunity satisfaction, leadership management satisfaction, and team cooperation atmosphere satisfaction are numbered as u 1 、u 2 、u 3 、u 4 、u 5 respectively, and the R & D investment ratio, number of patents, new product revenue ratio, and innovation culture atmosphere are numbered as R 1 、R 2 、R 3 、R 4 .

[0021] Preferably, the real - time monitoring and adjustment unit calculates the real - time monitoring and adjustment coefficient Zl according to the financial data and non - financial data, and its calculation formula is:

[0022]

[0023] In the formula, Zl represents the real-time monitoring adjustment coefficient, represents the total asset turnover rate, m 1 m 2 m 3 m 4 m 5 m 6 respectively represent the number of very satisfied customers, the number of satisfied customers, the number of average customers, the number of dissatisfied customers, the number of very dissatisfied customers and the total number of customers, m i represents the number of satisfied or dissatisfied customers for a certain item, x i represents the weight corresponding to the number of satisfied or dissatisfied customers for a certain item, u 1 u 2 u 3 u 4 u 5 respectively represent the satisfaction degree of salary and treatment, the satisfaction degree of working environment, the satisfaction degree of career development opportunities, the satisfaction degree of leadership and management, the satisfaction degree of team cooperation atmosphere, u i represents the job satisfaction of a certain employee, y i represents the corresponding weight of the job satisfaction of a certain employee, R 1 R 2 R 3 R 4 respectively represent the R & D investment ratio, the number of patents, the new product revenue ratio and the innovation culture atmosphere, R i represents a certain innovation ability of the company, z i represents the corresponding weight of a certain innovation ability of the company, b 1 b 2 respectively represent the proportions of financial data and non-financial data in the real-time monitoring adjustment coefficient, and k represents the market fluctuation adjustment factor.

[0024] Preferably, the risk assessment unit calculates the comprehensive data security assessment score Zo according to the financial risk identification index Za and non-financial data, and its calculation formula is:

[0025]

[0026] In the formula, Zo represents the comprehensive data security assessment score, Za represents the financial risk identification index, (100 - Za) represents the financial data score, m 1 m 2 m 3 m 4 m 5 m 6 respectively represent the number of very satisfied customers, the number of satisfied customers, the number of average customers, the number of dissatisfied customers, the number of very dissatisfied customers and the total number of customers, m iIndicates the number of satisfied or dissatisfied customers for a certain item, x i Indicates the weight corresponding to the number of satisfied or dissatisfied customers for a certain item, u 1 , u 2 , u 3 , u 4 , u 5 Respectively represent the satisfaction degree of salary and treatment, the satisfaction degree of working environment, the satisfaction degree of career development opportunities, the satisfaction degree of leadership management, the satisfaction degree of team cooperation atmosphere, u i Indicates the satisfaction degree of an employee towards their work, y i Indicates the corresponding weight of an employee's satisfaction degree towards their work, R 1 , R 2 , R 3 , R 4 Respectively represent the R & D investment ratio, the number of patents, the new product revenue ratio, and the innovation culture atmosphere, R i Indicates a certain innovation ability of the company, z i Indicates the corresponding weight of a certain innovation ability of the company Indicates the non - financial data score, c 1 , c 2 Respectively represent the weights of the financial data score and the non - financial data score in the comprehensive data security assessment score.

[0027] Preferably, the early - warning module includes an early - warning prompt unit and an early - warning emergency processing unit. The early - warning prompt unit compares the system - preset threshold value with the financial risk identification index Za. When the financial risk identification index Za exceeds 2 / 5 of the preset threshold value, it automatically gives a primary risk warning, prompts the relevant personnel in the user interface to pay primary attention to the financial data unit, and pulls this unit to the right side of the large screen for star - level marking.

[0028] Preferably, the early - warning emergency processing unit compares the system - preset threshold value with the financial risk identification index Za. When the financial risk identification index Za exceeds 4 / 5 of the preset threshold value, it automatically conducts emergency prevention processing, increasing the encryption algorithm process of the financial data unit while increasing the encryption key.

[0029] Preferably, the management response module adjusts the transmission frequency of the real - time monitoring device according to the magnitude of the real - time monitoring adjustment coefficient Zl, and adds a security firewall to the financial data unit, non - financial data unit, and network transmission process according to the comprehensive data security assessment score Zo.

[0030] Compared with the prior art, the present invention provides a financial risk identification method and system based on big data, having the following beneficial effects:

[0031] 1. Through calculating the financial risk identification index Za, the risk modeling unit can effectively evaluate the financial risk level of an enterprise, and based on the numerical range of the financial risk identification index Za, judge the financial condition of the enterprise, so as to take appropriate countermeasures. At the same time, combined with other financial analysis tools and the actual situation of the enterprise for comprehensive judgment, to improve the accuracy and reliability of the evaluation.

[0032] 2. By calculating the comprehensive data security evaluation score Zo, the risk assessment unit can comprehensively evaluate the data security status of an organization in both financial and non - financial aspects, which represents the comprehensiveness of data. This comprehensive evaluation method helps to distinguish security risks in financial or non - financial aspects, thereby improving the data security management level of the organization. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 is a schematic structural diagram of the present invention;

[0034] Figure 2 is a flowchart of the method steps of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0035] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0036] Please refer to Figure 1 - Figure 2 , a financial risk identification method based on big data, comprising the following steps:

[0037] Step 1: Establish a data source module, a risk analysis module, an early warning module, and a management response module;

[0038] Step 2: The data source module is divided into a financial data unit and a non - financial data unit;

[0039] Step 3: The risk analysis module is divided into a risk modeling unit, a real - time monitoring and adjustment unit, and a risk assessment unit, and calculate the financial risk identification index Za, the real - time monitoring and adjustment coefficient Zl, and the comprehensive data security evaluation score Zo;

[0040] Step 4: The early warning module is divided into an early warning prompt unit and an early warning emergency handling unit, and the early warning module is connected to the management response module through the network;

[0041] Step 5. The management response module adjusts the transmission frequency of the real-time monitoring device according to the size of the real-time monitoring adjustment coefficient Zl, and adds security firewalls to the financial data unit, non-financial data unit, and network transmission process according to the comprehensive data security assessment score Zo.

[0042] A financial risk identification system based on big data, including a data source module, a risk analysis module, an early warning module, and a management response module;

[0043] The data source module includes a financial data unit and a non-financial data unit. The financial data unit collects financial data through financial statements and external data sources, and the non-financial data unit collects non-financial data through third-party data services, social media, and news. The financial data unit and the non-financial data unit are connected to the risk analysis module through the network;

[0044] The risk analysis module includes a risk modeling unit, a real-time monitoring adjustment unit, and a risk assessment unit. The risk modeling unit calculates the financial risk identification index Za based on financial data. The real-time monitoring adjustment unit calculates the real-time monitoring adjustment coefficient Zl based on financial data and non-financial data. The risk assessment unit calculates the comprehensive data security assessment score Zo based on the financial risk identification index Za and non-financial data. The risk modeling unit, the real-time monitoring adjustment unit, and the risk assessment unit are connected to the early warning module through the network;

[0045] The early warning module includes an early warning prompt unit and an early warning emergency handling unit. The early warning module is connected to the management response module through the network.

[0046] The financial data unit numbers the sales revenue, working capital, retained earnings, earnings before interest and taxes, total market value of shareholders' equity, total liabilities, and total assets according to the characteristics of financial data. The sales revenue, working capital, retained earnings, earnings before interest and taxes, total market value of shareholders' equity, total liabilities, and total assets are numbered as W 1 、W 2 、W 3 、W 4 、W 5 、F、Q.

[0047] The risk modeling unit calculates the financial risk identification index Za based on financial data, and its calculation formula is:

[0048]

[0049] In the formula, Za represents the financial risk identification index, W 1 、W 2 、W 3 、W 4 、W 5, F, Q respectively represent sales revenue, working capital, retained earnings, earnings before interest and taxes, total market value of shareholders' equity, total liabilities, and total assets. represents the total asset turnover ratio. represents the liquidity of the company's assets. represents the cumulative profitability of the company. represents the company's ability to generate profits using all its assets. represents the financial structure, a 1 、a 2 、a 3 、a 4 、a 5 respectively represent the weighted coefficients of the total asset turnover ratio, the liquidity of the company's assets, the cumulative profitability of the company, the company's ability to generate profits using all its assets, and the financial structure in the financial risk identification index.

[0050] When the value of the financial risk identification index Za is smaller, it indicates a greater likelihood of the enterprise experiencing a financial crisis; when the value of the financial risk identification index Za is larger, it indicates a better financial health of the enterprise, a lower bankruptcy risk. When the value of the financial risk identification index Za calculated by the risk analysis module is between 2 and 2.6, it indicates that the enterprise is facing certain financial difficulties and needs to take emergency measures; when the value of the financial risk identification index Za is greater than 2.6, it indicates that the enterprise's financial condition is good, the possibility of bankruptcy is extremely small, and it can maintain the status quo.

[0051] The advantages are as follows: By calculating the financial risk identification index Za, the risk modeling unit can effectively evaluate the financial risk level of the enterprise, and based on the numerical range of the financial risk identification index Za, judge the financial condition of the enterprise, so as to make appropriate countermeasures. At the same time, combined with other financial analysis tools and the actual situation of the enterprise for comprehensive judgment to improve the accuracy and reliability of the evaluation.

[0052] The non-financial data unit numbers the number of very satisfied customers, satisfied customers, average customers, dissatisfied customers, very dissatisfied customers, total customers, salary treatment satisfaction, work environment satisfaction, career development opportunity satisfaction, leadership management satisfaction, team cooperation atmosphere satisfaction, R & D investment ratio, number of patents, new product revenue ratio, and innovation culture atmosphere according to non-financial data characteristics. The number of very satisfied customers, satisfied customers, average customers, dissatisfied customers, very dissatisfied customers, and total customers are numbered as m 1 、m 2 、m 3 、m 4 、m 5 、m 6, the satisfaction degrees of salary treatment, working environment, career development opportunities, leadership management, and team cooperation atmosphere are numbered as u 1 、u 2 、u 3 、u 4 、u 5 , the R & D investment ratio, the number of patents, the new product revenue ratio, and the innovation culture atmosphere are numbered as R 1 、R 2 、R 3 、R 4 .

[0053] The real-time monitoring and adjustment unit calculates the real-time monitoring and adjustment coefficient Zl based on financial data and non-financial data, and its calculation formula is:

[0054]

[0055] In the formula, Zl represents the real-time monitoring and adjustment coefficient, represents the total asset turnover rate, m 1 、m 2 、m 3 、m 4 、m 5 、m 6 respectively represent the number of very satisfied customers, the number of satisfied customers, the number of average customers, the number of dissatisfied customers, the number of very dissatisfied customers, and the total number of customers, m i represents the number of satisfied or dissatisfied customers for a certain item, x i represents the weight corresponding to the number of satisfied or dissatisfied customers for a certain item, u 1 、u 2 、u 3 、u 4 、u 5 respectively represent the satisfaction degrees of salary treatment, working environment, career development opportunities, leadership management, and team cooperation atmosphere, u i represents the job satisfaction of a certain employee, y i represents the corresponding weight of the job satisfaction of a certain employee, R 1 、R 2 、R 3 、R 4 respectively represent the R & D investment ratio, the number of patents, the new product revenue ratio, and the innovation culture atmosphere, R i represents a certain innovation ability of the company, z i represents the corresponding weight of a certain innovation ability of the company, b 1 、b 2 respectively represent the proportions of financial data and non-financial data in the real-time monitoring and adjustment coefficient, and k represents the market fluctuation adjustment factor.

[0056] The advantages are as follows: By calculating and monitoring the real-time adjustment coefficient Zl, when major changes occur in the market environment, the adjustment factor in the formula can adjust the volatility of the formula, enabling the system to respond promptly to changes in the external environment. Moreover, the adjustment factor can be flexibly adjusted according to strategic requirements and risk preferences to control the strictness of risk monitoring. By reasonably constructing and adjusting the calculation formula, the system can effectively monitor and respond to various financial risks, improving the efficiency and accuracy of risk management. The formula calculation of the present invention provides a real-time risk assessment tool for the system.

[0057] The risk assessment unit calculates the comprehensive data security assessment score Zo based on the financial risk identification index Za and non-financial data. The calculation formula is as follows:

[0058]

[0059] In the formula, Zo represents the comprehensive data security assessment score, Za represents the financial risk identification index, (100 - Za) represents the financial data score, m 1 , m 2 , m 3 , m 4 , m 5 , m 6 respectively represent the number of very satisfied customers, the number of satisfied customers, the number of average customers, the number of dissatisfied customers, the number of very dissatisfied customers, and the total number of customers. m i represents the number of satisfied or dissatisfied customers for a certain item, x i represents the weight corresponding to the number of satisfied or dissatisfied customers for a certain item, u 1 , u 2 , u 3 , u 4 , u 5 respectively represent the satisfaction degree of salary and benefits, the satisfaction degree of working environment, the satisfaction degree of career development opportunities, the satisfaction degree of leadership and management, the satisfaction degree of team cooperation atmosphere. u i represents the job satisfaction of a certain employee, y i represents the weight corresponding to the job satisfaction of a certain employee, R 1 , R 2 , R 3 , R 4 respectively represent the R & D investment ratio, the number of patents, the new product revenue ratio, and the innovation culture atmosphere. R i represents a certain innovation ability of the company, z i represents the weight corresponding to a certain innovation ability of the company, represents the non-financial data score, c 1 , c 2respectively represent the weights of the financial data score and the non-financial data score in the comprehensive data security assessment score.

[0060] Financial data score: This part of the score is mainly based on the amount of funds invested in data security and the proportion of data security investment in the total IT investment. The above indicators can reflect the degree of attention and actual investment of the organization in data security;

[0061] Non-financial data score: It includes various aspects such as the organization's management system, operation specifications, personnel training, and emergency response plan. This content is another aspect of ensuring data security.

[0062] The advantages are as follows: By calculating the comprehensive data security assessment score Zo, the risk assessment unit can comprehensively evaluate the data security status of the organization in terms of both finance and non-finance. The comprehensive assessment method of the present invention helps to distinguish security risks in the financial or non-financial aspects, thereby improving the data security management level of the organization.

[0063] The warning module includes a warning prompt unit and a warning emergency processing unit. The warning prompt unit compares the system preset threshold with the financial risk identification index Za. When the financial risk identification index Za exceeds 2 / 5 of the preset threshold, it automatically gives a primary risk warning, prompts the relevant personnel in the user interface to pay primary attention to the financial data unit, and pulls this unit to the right side of the large screen for star marking.

[0064] The warning emergency processing unit compares the system preset threshold with the financial risk identification index Za. When the financial risk identification index Za exceeds 4 / 5 of the preset threshold, it automatically performs emergency prevention processing, increasing the encryption algorithm process of the financial data unit and adding keys at the same time.

[0065] The management response module adjusts the transmission frequency of the real-time monitoring device according to the size of the real-time monitoring adjustment coefficient Zl, and adds security firewalls to the financial data unit, non-financial data unit, and network transmission process according to the comprehensive data security assessment score Zo.

[0066] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A financial risk identification method based on big data, characterized in that: The following steps are involved: Step 1: Establish data source module, risk analysis module, early warning module and management response module; Step 2: The data source module is divided into a financial data unit and a non-financial data unit; Step 3: The risk analysis module is divided into a risk modeling unit, a real-time monitoring adjustment unit, and a risk assessment unit, and calculates the financial risk identification index Za, the real-time monitoring adjustment coefficient Zl, and the comprehensive data security assessment score Zo; Step 4: The early warning module is divided into an early warning prompt unit and an early warning emergency processing unit, and the early warning module is connected to the management response module through a network; Step 5: The management response module adjusts the transmission frequency of the real-time monitoring device according to the size of the real-time monitoring adjustment coefficient Zl, and adds security protection walls to the financial data unit, non-financial data unit and network transmission process according to the comprehensive data security assessment score Zo.

2. A financial risk identification system based on big data, characterized in that: It includes data source module, risk analysis module, early warning module and management response module; The data source module includes a financial data unit and a non-financial data unit. The financial data unit collects financial data through financial statements and external data sources, and the non-financial data unit collects non-financial data through third-party data services, social media and news. The financial data unit and the non-financial data unit are connected to the risk analysis module through a network; The risk analysis module includes a risk modeling unit, a real-time monitoring adjustment unit and a risk assessment unit. The risk modeling unit calculates a financial risk identification index Za according to financial data, the real-time monitoring adjustment unit calculates a real-time monitoring adjustment coefficient Zl according to financial data and non-financial data, and the risk assessment unit calculates a comprehensive data security assessment score Zo according to the financial risk identification index Za and non-financial data. The risk modeling unit, the real-time monitoring adjustment unit and the risk assessment unit are connected to the early warning module through a network; The early warning module includes an early warning prompt unit and an early warning emergency processing unit, and the early warning module is connected to the management response module through a network.

3. The financial risk identification system based on big data according to claim 1 is characterized by: The financial data unit numbers the sales revenue, working capital, retained earnings, earnings before interest and taxes, the total market value of shareholders' equity, total liabilities and total assets according to the financial data characteristics, and the sales revenue, working capital, retained earnings, earnings before interest and taxes, the total market value of shareholders' equity, total liabilities and total assets are numbered W1, W2, W3, W4, W5, F, Q.

4. The financial risk identification system based on big data according to claim 3 is characterized by: The risk modeling unit calculates the financial risk identification index Za based on the financial data, and the calculation formula is: In the formula, Za represents the financial risk identification index, W1, W2, W3, W4, W5, F, and Q represent sales revenue, working capital, retained earnings, earnings before interest and taxes, total market value of shareholders' equity, total liabilities, and total assets, respectively. represents the total asset turnover ratio, Indicates the liquidity of the company's assets. Indicates the company's cumulative profitability. It indicates the ability of a company to use all its assets profitably. It represents the financial structure, a1, a2, a3, a4 and a5 represent the total asset turnover rate, the liquidity of the company's assets, the company's cumulative profitability, the company's ability to make profits by using all assets and the weighted coefficient of the financial structure in the financial risk identification index respectively.

5. The financial risk identification system based on big data according to claim 1 is characterized by: The non-financial data unit numbers the number of very satisfied customers, the number of satisfied customers, the number of general customers, the number of dissatisfied customers, the number of very dissatisfied customers, the total number of customers, the salary satisfaction, the work environment satisfaction, the career development opportunity satisfaction, the leadership management satisfaction, the teamwork atmosphere satisfaction, the R&D investment ratio, the number of patents, the new product revenue ratio and the innovative cultural atmosphere according to the non-financial data characteristics. The number of very satisfied customers, the number of satisfied customers, the number of general customers, the number of dissatisfied customers, the number of very dissatisfied customers and the total number of customers are numbered m1, m2, m3, m4, m5 and m6 respectively; the salary satisfaction, the work environment satisfaction, the career development opportunity satisfaction, the leadership management satisfaction and the teamwork atmosphere satisfaction are numbered u1, u2, u3, u4 and u5 respectively; the R&D investment ratio, the number of patents, the new product revenue ratio and the innovative cultural atmosphere are numbered R1, R2, R3 and R4 respectively.

6. The financial risk identification system based on big data according to claim 5 is characterized by: The real-time monitoring adjustment unit calculates the real-time monitoring adjustment coefficient Zl according to the financial data and the non-financial data, and the calculation formula is: In the formula, Zl represents the real-time monitoring adjustment coefficient. represents the total asset turnover rate, m1, m2, m3, m4, m5, and m6 represent the number of very satisfied customers, the number of satisfied customers, the number of average customers, the number of dissatisfied customers, the number of very dissatisfied customers, and the total number of customers, respectively. i Indicates the number of satisfied or dissatisfied customers, x i Indicates the weight corresponding to the number of satisfied or dissatisfied customers. u1, u2, u3, u4, and u5 represent satisfaction with salary, satisfaction with work environment, satisfaction with career development opportunities, satisfaction with leadership and management, and satisfaction with teamwork atmosphere, respectively. i Indicates an employee's job satisfaction, y i represents the corresponding weight of an employee's job satisfaction, R1, R2, R3, and R4 represent the R&D investment ratio, number of patents, new product revenue ratio, and innovation culture atmosphere, respectively. i Indicates a company's innovation capability. i It represents the corresponding weight of a certain innovation capability of the company, b1 and b2 respectively represent the proportion of financial data and non-financial data in the real-time monitoring adjustment coefficient, and k represents the market volatility adjustment factor.

7. The financial risk identification system based on big data according to claim 5 is characterized by: The risk assessment unit calculates the comprehensive data security assessment score Zo based on the financial risk identification index Za and non-financial data, and the calculation formula is: In the formula, Zo represents the comprehensive data security assessment score, Za represents the financial risk identification index, 100-Za) represents the financial data score, m1, m2, m3, m4, m5, and m6 represent the number of very satisfied customers, the number of satisfied customers, the number of average customers, the number of dissatisfied customers, the number of very dissatisfied customers, and the total number of customers, respectively. i Indicates the number of satisfied or dissatisfied customers, x i Indicates the weight corresponding to the number of satisfied or dissatisfied customers. u1, u2, u3, u4, and u5 represent satisfaction with salary, satisfaction with work environment, satisfaction with career development opportunities, satisfaction with leadership and management, and satisfaction with teamwork atmosphere, respectively. i Indicates an employee's job satisfaction, y i represents the corresponding weight of an employee's job satisfaction, R1, R2, R3, and R4 represent the R&D investment ratio, number of patents, new product revenue ratio, and innovation culture atmosphere, respectively. i Indicates a company's innovation capability. i Indicates the corresponding weight of a company's innovation capability. represents the non-financial data score, c1 and c2 represent the weights of the financial data score and the non-financial data score in the comprehensive data security assessment score, respectively.

8. The financial risk identification system based on big data according to claim 7 is characterized by: The early warning module includes an early warning prompt unit and an early warning emergency processing unit. The early warning prompt unit compares the system preset zone value with the financial risk identification index Za. When the financial risk identification index Za exceeds 2 / 5 of the preset zone value, it automatically makes a primary risk warning, prompting relevant personnel of the user interface to pay primary attention to the financial data unit and pull the unit to the right side of the large screen for star marking.

9. The financial risk identification system based on big data according to claim 4 is characterized by: The early warning emergency processing unit compares the system preset zone value with the financial risk identification index Za. When the financial risk identification index Za exceeds 4 / 5 of the preset zone value, emergency prevention processing is automatically performed, and the encryption algorithm process of the financial data unit is increased while adding a key.

10. The financial risk identification system based on big data according to claim 7, characterized in that: The management response module adjusts the transmission frequency of the real-time monitoring device according to the size of the real-time monitoring adjustment coefficient Zl, and adds a security protection wall to the financial data unit, non-financial data unit and network transmission process according to the comprehensive data security assessment score Zo.

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