Risk identification method, device and equipment of manufacturing industry chain based on fund flow

Abstract

The invention provides a fund flow-based risk identification method, device and equipment for a manufacturing industry chain. The method comprises the steps of obtaining a fund flow data set of the manufacturing industry chain; determining a target evaluation index standard based on a pre-constructed fund risk evaluation standard and a fund flow data set; constructing a first-level risk index judgment matrix based on the first-level risk indexes, and constructing a second-level risk index judgment matrix based on the second-level risk indexes corresponding to the first-level risk indexes; determining risk data of each first-level risk index based on a preset assignment evaluation matrix and the second-level risk index judgment matrix of each first-level risk index, and determining a whole industry chain risk standard based on the first-level risk index judgment matrix and the risk data of each first-level risk index; and determining a target high-risk index. According to the embodiment of the invention, potential risks in the manufacturing industry chain can be identified more accurately, powerful support is provided for risk management of the industry chain, and risk early warning and effective prevention and control of the manufacturing industry chain are realized.

Description

Technical Field

[0001] The present disclosure relates to the technical field of risk identification, and more particularly, to a method, apparatus, and device for risk identification of a manufacturing industry chain based on cash flow. Background Art

[0002] As the global manufacturing industry chain faces multiple dilemmas such as capital shortage, inventory backlog, volatile energy prices, and significantly increased operational risks. These external shocks not only affect the stable operation of the industry chain but also exacerbate the uncertainty of competition and cooperation among enterprises. In particular, the linear structure and geographical agglomeration characteristics of the industry chain make risks highly contagious within the industry chain. Once a problem occurs in a certain link, it is very likely to spread rapidly throughout the network, causing serious damage to the entire manufacturing industry chain and affecting the healthy development of the global economy.

[0003] In the related art, risk identification of the industry chain mostly focuses on issues such as supply chain interruption and raw material supply risks, while ignoring the key role of cash flow transmission in the industry chain. The instability of cash flow may lead to enterprises being unable to pay suppliers on time or unable to obtain sufficient financing support, thereby triggering more complex chain reactions. Summary of the Invention

[0004] Embodiments of the present disclosure at least provide a method, apparatus, and device for risk identification of a manufacturing industry chain based on cash flow. By comprehensively using cash flow data and a multi-level risk index judgment matrix, potential risks in the manufacturing industry chain can be identified more accurately, providing strong support for industry chain risk management, thereby realizing risk early warning and effective prevention and control of the manufacturing industry chain.

[0005] Embodiments of the present disclosure provide a method for risk identification of a manufacturing industry chain based on cash flow, including:

[0006] Obtaining a cash flow data set of the manufacturing industry chain; and determining a target evaluation index standard based on a pre-constructed capital risk evaluation standard and the cash flow data set; wherein the target evaluation index standard includes a plurality of primary risk indicators, and each primary risk indicator corresponds to a plurality of secondary risk indicators;

[0007] Constructing a primary risk indicator judgment matrix based on the plurality of primary risk indicators; and constructing a secondary risk indicator judgment matrix corresponding to each primary risk indicator based on the plurality of secondary risk indicators corresponding to each primary risk indicator;

[0008] Determining risk data corresponding to each primary risk indicator based on a preset assignment evaluation matrix and the secondary risk indicator judgment matrix corresponding to each primary risk indicator; and determining an industry chain-wide risk standard based on the primary risk indicator judgment matrix and the risk data corresponding to each primary risk indicator;

[0009] Determine the target high-risk indicators based on the risk data corresponding to each first-level risk indicator and the whole industrial chain risk standard.

[0010] In some possible embodiments, the capital flow data set includes capital flow data corresponding to different years; the determining of the target evaluation indicator standard based on the pre-constructed capital risk evaluation standard and the capital flow data set includes:

[0011] Obtain the capital transaction information about the manufacturing industry chain, and determine a set of capital risk indicators based on the capital transaction information; wherein, the set of capital risk indicators includes a plurality of first-level risk indicators, and each first-level risk indicator corresponds to a plurality of second-level risk indicators;

[0012] Determine the capital flow data set to be processed based on the set of capital risk indicators and the capital flow data set; wherein, the capital flow data set to be processed includes the capital flow data of different years corresponding to each second-level risk indicator;

[0013] Determine the difference degree value of each second-level risk indicator based on the difference degree formula and the capital flow data set to be processed; and calculate the weight of each second-level risk indicator respectively based on the weight calculation formula and the difference degree value of each second-level risk indicator.

[0014] For each second-level risk indicator, calculate the correlation between the second-level risk indicator and other second-level risk indicators respectively; and screen the set of capital risk indicators based on the calculation result and the weight of the second-level risk indicator to obtain the target evaluation indicator standard.

[0015] In some possible embodiments, the difference degree formula includes:

[0016]

[0017] wherein, d j represents the difference degree value of the jth second-level risk indicator; m represents the total number of years; P ij represents the normalized capital flow data of the jth second-level risk indicator in the ith year;

[0018] The weight calculation formula includes:

[0019]

[0020] wherein, W j represents the weight of the jth risk indicator; l represents the number of second-level risk indicators included in the first-level risk indicator to which the jth second-level risk indicator belongs.

[0021] In some possible embodiments, constructing a first-level risk index judgment matrix based on the multiple first-level risk indexes includes:

[0022] Combining the multiple first-level risk indexes in pairs respectively to obtain multiple pairs of first-level risk indexes;

[0023] Generating a first-level index importance scoring questionnaire based on the multiple pairs of first-level risk indexes, and sending the first-level index importance scoring questionnaire to the receiving terminals corresponding to multiple relevant personnel respectively;

[0024] Receiving the scoring results of the first-level index importance scoring questionnaire by multiple relevant personnel according to the preset assignment scoring rules, and constructing a first-level risk index judgment matrix based on the scoring results of the first-level index importance scoring questionnaire by the multiple relevant personnel.

[0025] In some possible embodiments, constructing a second-level risk index judgment matrix corresponding to each first-level risk index based on the multiple second-level risk indexes corresponding to each first-level risk index includes:

[0026] For each first-level risk index, combining the multiple second-level risk indexes corresponding to the first-level risk index in pairs respectively to obtain multiple pairs of second-level risk indexes;

[0027] Generating a second-level index importance scoring questionnaire based on the multiple pairs of second-level risk indexes, and sending the second-level index importance scoring questionnaire to the receiving terminals corresponding to multiple relevant personnel respectively;

[0028] Receiving the scoring results of the second-level index importance scoring questionnaire by multiple relevant personnel according to the preset assignment scoring rules, and constructing a second-level risk index judgment matrix based on the scoring results of the second-level index importance scoring questionnaire by the multiple relevant personnel.

[0029] In some possible embodiments, determining the risk data corresponding to each first-level risk index based on the preset assignment evaluation matrix and the second-level risk index judgment matrix corresponding to each first-level risk index includes:

[0030] Calculating the risk index weights corresponding to each second-level risk index judgment matrix, and determining a risk index weight matrix corresponding to each first-level risk index based on the risk index weights corresponding to each second-level risk index judgment matrix;

[0031] Calculating the score membership degrees corresponding to each second-level risk index judgment matrix, and determining a score fuzzy matrix corresponding to each first-level risk index based on the score membership degrees corresponding to each second-level risk index judgment matrix;

[0032] For each first-level risk indicator, determine the risk data corresponding to the first-level risk indicator based on a preset assignment evaluation matrix, the risk indicator weight matrix, and the score fuzzy matrix.

[0033] In some possible embodiments, determining the full industrial chain risk standard based on the first-level risk indicator judgment matrix and the risk data corresponding to each first-level risk indicator includes:

[0034] Calculate the risk indicator weights corresponding to the first-level risk indicator judgment matrix, and determine the full industrial chain risk standard based on the settlement result and the risk data corresponding to each first-level risk indicator.

[0035] An embodiment of the present disclosure provides a risk identification device for a manufacturing industry chain based on cash flow, including:

[0036] An index standard determination module, configured to obtain a cash flow data set of the manufacturing industry chain; and determine a target evaluation index standard based on a pre-constructed fund risk evaluation standard and the cash flow data set; wherein, the target evaluation index standard includes multiple first-level risk indicators, and each first-level risk indicator corresponds to multiple second-level risk indicators;

[0037] A judgment matrix construction module, configured to construct a first-level risk indicator judgment matrix based on the multiple first-level risk indicators; and construct a second-level risk indicator judgment matrix corresponding to each first-level risk indicator based on the multiple second-level risk indicators corresponding to each first-level risk indicator;

[0038] A risk data calculation module, configured to determine the risk data corresponding to each first-level risk indicator based on a preset assignment evaluation matrix and the second-level risk indicator judgment matrix corresponding to each first-level risk indicator; and determine the full industrial chain risk standard based on the first-level risk indicator judgment matrix and the risk data corresponding to each first-level risk indicator;

[0039] A high-risk indicator determination module, configured to determine a target high-risk indicator based on the risk data corresponding to each first-level risk indicator and the full industrial chain risk standard.

[0040] In some possible embodiments, the cash flow data set includes cash flow data corresponding to different years; the index standard determination module is specifically configured to:

[0041] Obtain the fund transaction information about the manufacturing industry chain, and determine a fund risk indicator set based on the fund transaction information; wherein, the fund risk indicator set includes multiple first-level risk indicators, and each first-level risk indicator corresponds to multiple second-level risk indicators;

[0042] Determine the fund flow data set to be processed based on the set of fund risk indicators and the fund flow data set; wherein, the fund flow data set to be processed includes fund flow data for different years corresponding to each secondary risk indicator.

[0043] Determine the difference degree value of each secondary risk indicator based on the difference degree formula and the fund flow data set to be processed; and calculate the weight of each secondary risk indicator respectively based on the weight calculation formula and the difference degree value of each secondary risk indicator.

[0044] For each secondary risk indicator, calculate the correlation between the secondary risk indicator and other secondary risk indicators respectively; and screen the set of fund risk indicators based on the calculation result and the weight of the secondary risk indicator to obtain the target evaluation index standard.

[0045] In some possible embodiments, the difference degree formula includes:

[0046]

[0047] where d j represents the difference degree value of the j-th secondary risk indicator; m represents the total number of years; P ij represents the normalized fund flow data of the j-th secondary risk indicator in the i-th year.

[0048] The weight calculation formula includes:

[0049]

[0050] where W j represents the weight of the j-th risk indicator; l represents the number of secondary risk indicators included in the primary risk indicator to which the j-th secondary risk indicator belongs.

[0051] In some possible embodiments, the judgment matrix construction module is specifically configured to:

[0052] Combine the multiple primary risk indicators in pairs respectively to obtain multiple pairs of primary risk indicators;

[0053] Generate a primary index importance scoring questionnaire based on the multiple pairs of primary risk indicators, and send the primary index importance scoring questionnaire to the receiving terminals corresponding to multiple relevant personnel respectively;

[0054] Receive the scoring results of the multiple relevant personnel on the primary index importance scoring questionnaire according to the preset assignment scoring rules, and construct a primary risk indicator judgment matrix based on the scoring results of the multiple relevant personnel on the primary index importance scoring questionnaire.

[0055] In some possible embodiments, the judgment matrix construction module is specifically configured to:

[0056] For each first-level risk indicator, pairwise combine multiple second-level risk indicators corresponding to the first-level risk indicator to obtain multiple pairs of second-level risk indicators;

[0057] Generate a second-level indicator importance scoring questionnaire based on the multiple pairs of second-level risk indicators, and send the second-level indicator importance scoring questionnaire to the receiving terminals corresponding to multiple relevant personnel respectively;

[0058] Receive the scoring results of the second-level indicator importance scoring questionnaire by multiple relevant personnel according to the preset assignment scoring rules, and construct a second-level risk indicator judgment matrix based on the scoring results of the second-level indicator importance scoring questionnaire by the multiple relevant personnel.

[0059] In some possible embodiments, the risk data calculation module is specifically configured to:

[0060] Calculate the risk indicator weight corresponding to each second-level risk indicator judgment matrix, and determine the risk indicator weight matrix corresponding to each first-level risk indicator based on the risk indicator weight corresponding to each second-level risk indicator judgment matrix;

[0061] Calculate the score membership degree corresponding to each second-level risk indicator judgment matrix, and determine the score fuzzy matrix corresponding to each first-level risk indicator based on the score membership degree corresponding to each second-level risk indicator judgment matrix;

[0062] For each first-level risk indicator, determine the risk data corresponding to the first-level risk indicator based on the preset assignment evaluation matrix, the risk indicator weight matrix, and the score fuzzy matrix.

[0063] In some possible embodiments, the risk data calculation module is specifically configured to:

[0064] Calculate the risk indicator weight corresponding to the first-level risk indicator judgment matrix, and determine the full-industry-chain risk standard based on the settlement result and the risk data corresponding to each first-level risk indicator.

[0065] An embodiment of the present disclosure provides a computer device, including: a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the computer device runs, the processor communicates with the memory through the bus. When the machine-readable instructions are executed by the processor, the risk identification method of the manufacturing industry chain based on the cash flow as described in any of the above possible implementation manners is executed.

[0066] An embodiment of the present disclosure provides a computer-readable storage medium, on which a computer program is stored. When the computer program is run by a processor, it implements the risk identification method for the manufacturing industry chain based on the cash flow as described in any of the above possible implementation manners.

[0067] In the embodiment of the present disclosure, the risk identification method, device and equipment for the manufacturing industry chain based on the cash flow are provided. Specifically, first, a cash flow data set of the manufacturing industry chain is obtained; and based on the pre-constructed cash risk evaluation criteria and the cash flow data set, the target evaluation index criteria are determined; secondly, a first-level risk index judgment matrix is constructed based on multiple first-level risk indicators; and, a second-level risk index judgment matrix corresponding to each first-level risk indicator is constructed based on multiple second-level risk indicators corresponding to each first-level risk indicator; then, based on the preset assignment evaluation matrix and the second-level risk index judgment matrix corresponding to each first-level risk indicator, the risk data corresponding to each first-level risk indicator is determined; and based on the first-level risk index judgment matrix and the risk data corresponding to each first-level risk indicator, the whole industry chain risk criteria are determined; finally, based on the risk data corresponding to each first-level risk indicator and the whole industry chain risk criteria, the target high-risk indicators are determined. In this way, by comprehensively using the cash flow data and the multi-level risk index judgment matrix, the potential risks in the manufacturing industry chain can be identified more accurately, providing strong support for the risk management of the industry chain, so as to realize the risk early warning and effective prevention and control of the manufacturing industry chain.

[0068] To make the above objects, features and advantages of the present disclosure more obvious and understandable, the following specific preferred embodiments are given and described in detail in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the accompanying drawings required to be cited in the embodiments will be briefly introduced below. The accompanying drawings herein are incorporated into the specification and form a part of this specification. These drawings show the embodiments conforming to the present disclosure and are used together with the specification to explain the technical solutions of the present disclosure. It should be understood that the following drawings only show some embodiments of the present disclosure and should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0070] Figure 1 Shows a flowchart of a risk identification method for a manufacturing industry chain based on cash flow provided by an embodiment of the present disclosure;

[0071] Figure 2 Shows a flowchart of a method for determining target evaluation index criteria provided by an embodiment of the present disclosure;

[0072] Figure 3Shows a flowchart of a method for constructing a first-level risk index judgment matrix provided by an embodiment of the present disclosure;

[0073] Figure 4 Shows a flowchart of a method for constructing a second-level risk index judgment matrix provided by an embodiment of the present disclosure;

[0074] Figure 5 Shows a flowchart of a method for determining risk data provided by an embodiment of the present disclosure;

[0075] Figure 6 Shows a schematic structural diagram of a risk identification device for a manufacturing industry chain based on cash flow provided by an embodiment of the present disclosure;

[0076] Figure 7 Shows a schematic structural diagram of a computer device provided by an embodiment of the present disclosure. Detailed implementation manners

[0077] To make the objectives, technical solutions, and advantages of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present disclosure. Apparently, the described embodiments are only some of the embodiments of the present disclosure, rather than all the embodiments. Usually, the components of the embodiments of the present disclosure described and illustrated in the accompanying drawings herein can be arranged and designed in various different configurations. Therefore, the detailed description of the embodiments of the present disclosure provided in the accompanying drawings below is not intended to limit the scope of the present disclosure claimed, but merely represents selected embodiments of the present disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of the present disclosure without creative efforts fall within the scope of protection of the present disclosure.

[0078] It should be noted that: similar reference numerals and letters denote similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0079] The term "and / or" in this article merely describes an association relationship and indicates that three relationships may exist. For example, A and / or B may represent: A exists alone, both A and B exist simultaneously, and B exists alone. In addition, the term "at least one" in this article means any one of a plurality or any combination of at least two of a plurality. For example, including at least one of A, B, and C may represent including any one or more elements selected from the set composed of A, B, and C.

[0080] For the convenience of understanding this embodiment, the execution subject of the risk identification method for the manufacturing industry chain based on the capital flow provided by the embodiments of the present disclosure will be introduced in detail first. The execution subject of the risk identification method for the manufacturing industry chain based on the capital flow provided by the embodiments of the present disclosure is a computer device. This computer device can be a server. Among them, the server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud storage, big data, and artificial intelligence platforms. Optionally, this method can also be applied to an implementation environment composed of a computer device and a server.

[0081] The following will describe in detail the risk identification method for the manufacturing industry chain based on the capital flow provided by the embodiments of the present application with reference to the accompanying drawings. Refer to Figure 1 As shown, it is a flowchart of a risk identification method for the manufacturing industry chain based on the capital flow provided by the embodiments of the present disclosure. This method includes the following S101 to S104:

[0082] S101, obtain the capital flow data set of the manufacturing industry chain; and determine the target evaluation index standard based on the pre-constructed capital risk evaluation standard and the capital flow data set.

[0083] It can be understood that the capital flow data refers to the flow of funds between enterprises and in the upstream and downstream industry chains, including the capital flow direction and flow volume in aspects such as enterprise income, expenditure, borrowing, and investment, which can help evaluate the health status of enterprise operations. Here, the capital flow data set includes the capital flow data corresponding to different years. The manufacturing industry chain, as a complex system, involves multiple links from raw material supply, product production to product sales. In this chain, the flow of funds reflects the operation efficiency and health status of each link in the industry chain. Therefore, in-depth analysis of the capital flow data of the manufacturing industry chain helps to discover potential capital risks and optimize the capital allocation of the industry chain.

[0084] Specifically, referring to Figure 2 As shown, when determining the target evaluation index standard based on the pre-constructed capital risk evaluation standard and the capital flow data set, the following steps S201 to S204 can be included:

[0085] S201, obtain the capital transaction information about the manufacturing industry chain, and determine the capital risk index set based on the capital transaction information.

[0086] It is understandable that the fund transaction information includes transaction records, payment information, borrowing situations, etc. of all enterprises in the industrial chain. The set of fund risk indicators includes multiple primary risk indicators, and each primary risk indicator corresponds to multiple secondary risk indicators. The primary indicators are the identifiers for the macro-classification of the fund risks in the industrial chain, and the secondary indicators are the further refinement and quantification of the primary risk indicators.

[0087] Here, the primary risk indicators proposed in this disclosure may include transaction, transportation, warehousing, customs clearance, and financing. Among them, transaction mainly involves the fund transactions between an enterprise and other enterprises or the market, including procurement, sales, etc. Transportation refers to the fund flow and logistics costs involved in the process of products from the production link to the final consumer or the sales link. Warehousing mainly involves the fund occupation of product inventory, warehousing costs, etc. Customs clearance is related to the fund flow in the import and export links, covering tariffs, transportation costs, and related fees, etc. Financing refers to the ways and means for an enterprise to obtain funds during its operation, such as borrowing, financing costs, etc.

[0088] Specifically, the secondary risk indicators corresponding to the primary risk indicator of transaction may include transaction price volatility, accounts payable turnover rate, accounts receivable turnover rate; the secondary risk indicators corresponding to the primary risk indicator of transportation may include total transportation costs, inventory turnover rate, profit change; the secondary risk indicators corresponding to the primary risk indicator of warehousing may include warehousing costs, market price fluctuation degree, inventory turnover rate; the secondary risk indicators corresponding to the primary risk indicator of customs clearance may include customs clearance fees, exchange rate fluctuation degree, service provider credit rating; the secondary risk indicators corresponding to the primary risk indicator of financing may include financing costs, actual loan interest rate, return on total assets, accounts receivable turnover rate, cash flow ratio, asset-liability ratio, credit rating.

[0089] In some other embodiments, the indicators in the set of fund risk indicators can also be extended and customized according to the characteristics of the specific industrial chain and the risk assessment requirements, such as adding indicators reflecting supply chain collaboration efficiency, market supply and demand changes, etc., which are not specifically limited herein.

[0090] Exemplarily, after the fund transaction information of the manufacturing industry chain in this disclosure, the expert scoring method is also used to correct the set of indicators corresponding to the fund transaction information based on the obtained information. This disclosure selects financial and domain experts and invites these experts to score the importance of the alternative indicators through the questionnaire survey method. Finally, the average score of each indicator is taken. For the selection of each channel risk indicator, if the score of this indicator is above 2.5 points, it will be temporarily included in the consideration scope. On the basis of reading and analyzing a large number of reference documents, the suggestions of professionals are collected, and the principles of indicator selection are followed. Since the final results of the expert scoring show that the scores of all indicators are above 2.5 points, all indicators are temporarily included in the consideration scope.

[0091] S202. Determine the fund flow data set to be processed based on the set of fund risk indicators and the fund flow data set.

[0092] Among them, the fund flow data set to be processed includes the fund flow data of different years corresponding to each secondary risk indicator. The fund flow data set to be processed obtained in this disclosure is shown in Table 1:

[0093] Table 1

[0094]

[0095]

[0096]

[0097] S203. Determine the difference degree value of each secondary risk indicator based on the difference degree formula and the fund flow data set to be processed; and calculate the weight of each secondary risk indicator respectively based on the weight calculation formula and the difference degree value of each secondary risk indicator.

[0098] It can be understood that the difference degree value reflects the fluctuation degree or change magnitude of each secondary risk indicator between different years. By calculating the difference degree value, those indicators with large fluctuations between different years and potentially containing greater risks can be identified, which may represent the instability or potential problems of certain links in the industrial chain. The weight reflects the relative position or contribution degree of each secondary risk indicator in the overall fund risk evaluation. The calculation of the weight is based on the difference degree value, because the greater the difference degree of an indicator, the greater the impact of its change on the overall fund risk may be, so a higher weight should be given.

[0099] Here, the difference degree formula can be expressed as:

[0100]

[0101] where d j represents the difference degree value of the jth secondary risk indicator; m represents the total number of years; P ij represents the normalized fund flow data of the jth secondary risk indicator in the ith year.

[0102] Here, the weight calculation formula can be expressed as:

[0103]

[0104] where W j represents the weight of the jth risk indicator; l represents the number of secondary risk indicators included in the primary risk indicator to which the jth secondary risk indicator belongs.

[0105] S204. For each secondary risk indicator, calculate the correlation between the secondary risk indicator and other secondary risk indicators respectively; and screen the set of capital risk indicators based on the calculation results and the weights of the secondary risk indicators to obtain the target evaluation index standard.

[0106] Specifically, after obtaining the weights corresponding to each secondary risk indicator, in order to further optimize the set of capital risk indicators and improve the accuracy and efficiency of evaluation, the correlation between each secondary risk indicator needs to be considered. There may be a high correlation between some indicators, that is, their change trends and fluctuations are similar, which means they may reflect the same or similar risk factors. In this case, if these highly correlated indicators are selected as evaluation indicators at the same time, it will not only increase the complexity of evaluation, but also may reduce the accuracy of evaluation due to information redundancy.

[0107] Therefore, to solve this problem, the correlation between each secondary risk indicator and other secondary risk indicators can be calculated, and it can be screened according to the weights between each secondary risk indicator. Various statistical methods can be used to calculate the correlation, such as Pearson correlation coefficient, Spearman rank correlation coefficient, etc.

[0108] Here, the present disclosure selects the Person correlation coefficient as the metric, which is used to evaluate the linear correlation degree between two variables. The calculation formula of the Person correlation coefficient is:

[0109]

[0110] Among them, cov(X,Y) is the covariance between the secondary risk indicator X and the secondary risk indicator Y, and σX and σX are the standard deviations of X and Y respectively. The value range of the Person correlation coefficient is [-1,1], where 1 represents a perfect positive correlation, -1 represents a perfect negative correlation, and 0 represents no correlation.

[0111] Exemplarily, after calculating the correlation between each secondary risk indicator and other secondary risk indicators, the set of capital risk indicators can be screened according to the calculation results and the weights of the secondary risk indicators. Specifically, the set of capital risk indicators includes the following three types of indicators: indicators with high correlation and high weight: select indicators with a correlation coefficient ≥ 0.9 and a relatively high weight. These indicators are not only highly correlated with other indicators but also play an important role in the overall evaluation, so they can be regarded as representative indicators; indicators with low correlation but high weight: for indicators with a correlation coefficient < 0.9, if their weight ≥ 0.20, they are retained. Although the correlation of these indicators with other indicators is not high, their importance in the overall evaluation cannot be ignored; eliminating redundant indicators: for indicators with a correlation coefficient ≥ 0.9 but a low weight, and indicators with a correlation coefficient < 0.9 and a weight < 0.20, they are eliminated. These indicators are either highly repetitive with other indicators or have a low importance in the overall evaluation, so they can be regarded as redundant. After screening the set of capital risk indicators according to the above method, the target evaluation indicator standard can be obtained. Among them, the target evaluation indicator standard includes multiple primary risk indicators, and each primary risk indicator corresponds to multiple secondary risk indicators.

[0112] In some other embodiments, the screening rules can also be set according to specific requirements, which are not specifically limited here.

[0113] S102, constructing a primary risk indicator judgment matrix based on the multiple primary risk indicators; and constructing a secondary risk indicator judgment matrix corresponding to each primary risk indicator based on the multiple secondary risk indicators corresponding to each primary risk indicator.

[0114] Exemplarily, referring to Figure 3 as shown, when constructing a primary risk indicator judgment matrix based on multiple primary risk indicators, the following steps S301 - S303 can be included:

[0115] S301, respectively combining the multiple primary risk indicators in pairs to obtain multiple pairs of primary risk indicators.

[0116] Here, combining the multiple primary risk indicators in pairs to obtain multiple pairs of primary risk indicators, these combinations are the basis of risk assessment, and the relationship between every two primary risk indicators will be clearly shown, preparing for the subsequent assessment work.

[0117] S302, generating a primary indicator importance scoring questionnaire based on the multiple pairs of primary risk indicators, and sending the primary indicator importance scoring questionnaire to the receiving terminals corresponding to multiple relevant personnel respectively.

[0118] It is understandable that based on the above-mentioned multiple pairs of first-level risk indicators, a questionnaire for scoring the importance of the first-level indicators is generated and sent to the receiving terminals of multiple relevant personnel respectively, aiming to evaluate the relative importance between each pair of first-level risk indicators by collecting the opinions of each relevant person.

[0119] S303. Receive the scoring results of the questionnaire for scoring the importance of the first-level indicators by multiple relevant personnel according to the preset assignment scoring rules, and construct a judgment matrix for the first-level risk indicators based on the scoring results of the questionnaire for scoring the importance of the first-level indicators by the multiple relevant personnel.

[0120] Specifically, relevant personnel score the questionnaire for scoring the importance of the first-level indicators according to the preset assignment scoring rules and feedback the scoring results to the system. The system finally constructs a judgment matrix for the first-level risk indicators based on the scoring results of all relevant personnel. This matrix is the basis for subsequent risk management decisions. By quantifying the evaluation results, it clarifies the relative importance between different first-level risk indicators. Here, the preset assignment scoring rules in the present disclosure refer to that relevant personnel use the 1-5 classification method to score each pair of first-level risk indicators.

[0121] Exemplarily, when constructing the judgment matrix for the second-level risk indicators, the process also follows the similar above-mentioned steps S301 to S303. Referring to Figure 4 As shown, when constructing the judgment matrix for the second-level risk indicators corresponding to each first-level risk indicator based on the multiple second-level risk indicators corresponding to each first-level risk indicator, the following steps S401 to S403 may be included:

[0122] S401. For each first-level risk indicator, respectively combine the multiple second-level risk indicators corresponding to the first-level risk indicator in pairs to obtain multiple pairs of second-level risk indicators.

[0123] Here, the method of step S401 is the same as the principle of the method of the above-mentioned step S301, only the objects are different. Therefore, for details, please refer to step S301 and will not be elaborated here.

[0124] S402. Generate a questionnaire for scoring the importance of the second-level indicators based on the multiple pairs of second-level risk indicators, and send the questionnaire for scoring the importance of the second-level indicators to the receiving terminals corresponding to multiple relevant personnel respectively.

[0125] Specifically, based on the above-mentioned multiple pairs of second-level risk indicators, a questionnaire for scoring the importance of the second-level indicators is generated and sent to the receiving terminals of multiple relevant personnel respectively. Step S402 is similar to the scoring link of the first-level risk indicators. By collecting the feedback of each relevant person, it ensures that the importance of each second-level risk indicator has been fully evaluated.

[0126] S403. Receive the scoring results of the importance scoring questionnaire for the secondary indicators by multiple relevant personnel according to the preset assignment scoring rules, and construct a secondary risk indicator judgment matrix based on the scoring results of the importance scoring questionnaire for the secondary indicators by the multiple relevant personnel.

[0127] Here, the relevant personnel score the importance scoring questionnaire for the secondary indicators according to the preset assignment scoring rules, and construct a secondary risk indicator judgment matrix based on the scoring results of all relevant personnel. In this way, it can help decision-makers understand the roles and priorities of different secondary risk indicators in the overall risk management framework, and provide clear guidance for further risk analysis and response strategies.

[0128] S103. Determine the risk data corresponding to each primary risk indicator based on the preset assignment evaluation matrix and the secondary risk indicator judgment matrix corresponding to each primary risk indicator; and determine the whole industry chain risk standard based on the primary risk indicator judgment matrix and the risk data corresponding to each primary risk indicator.

[0129] Here, the preset assignment evaluation matrix is determined by the preset assignment scoring rules. Since the assignment scoring rules in this disclosure are set as 1-5 classification scoring, the preset assignment evaluation matrix is expressed as [1, 2, 3, 4, 5].

[0130] Exemplarily, referring to Figure 5 As shown, when determining the risk data corresponding to each primary risk indicator based on the preset assignment evaluation matrix and the secondary risk indicator judgment matrix corresponding to each primary risk indicator, the following S501-S503 may be included:

[0131] S501. Calculate the risk indicator weights corresponding to each secondary risk indicator judgment matrix, and determine the risk indicator weight matrix corresponding to each primary risk indicator based on the risk indicator weights corresponding to each secondary risk indicator judgment matrix.

[0132] Specifically, perform a consistency test on each secondary risk indicator judgment matrix, find the maximum eigenvalue λ max of the judgment matrix, and substitute the maximum eigenvalue into the formula to obtain the CI value, look up the RI value in the table, and calculate the consistency ratio CR value according to the formula to determine the risk indicator weights corresponding to each secondary risk indicator judgment matrix.

[0133] It can be understood that after calculating the risk indicator weights corresponding to each secondary risk indicator judgment matrix, they can be combined to obtain the risk indicator weight matrix corresponding to each primary risk indicator.

[0134] S502. Calculate the score membership degree corresponding to each of the secondary risk index judgment matrices, and determine the score fuzzy matrix corresponding to each primary risk index based on the score membership degree corresponding to each of the secondary risk index judgment matrices.

[0135] Here, calculating the score membership degree corresponding to each secondary risk index judgment matrix is based on the scoring results of a questionnaire on the importance degree of the secondary indicators scored by multiple relevant personnel according to a preset assignment scoring rule. Specifically, based on the scoring results, the frequency of each secondary risk index at each score level is calculated, and the frequency of each level is divided by the total number of times the index is scored, that is, the membership degree of each index corresponding to each level is calculated according to the fuzzy statistical method. Furthermore, the score fuzzy matrix corresponding to each primary risk index is determined based on the score membership degree corresponding to each secondary risk index judgment matrix.

[0136] S503. For each primary risk index, determine the risk data corresponding to the primary risk index based on a preset assignment evaluation matrix, the risk index weight matrix, and the score fuzzy matrix.

[0137] Here, the weighted average method is used in the present disclosure to calculate the risk data corresponding to each primary risk index, and the calculation formula can be expressed as:

[0138] B o =(A o R o )V T ;

[0139] Wherein, B o represents the risk data corresponding to the o-th primary risk index; A o represents the risk index weight matrix corresponding to the o-th primary risk index; R o represents the score fuzzy matrix corresponding to the o-th primary risk index; V represents the preset assignment evaluation matrix.

[0140] S104. Determine the target high-risk indicators based on the risk data corresponding to each primary risk index and the whole industrial chain risk standard.

[0141] It can be understood that calculating the risk index weights corresponding to the first-level risk index judgment matrix according to the method in step S501, and determining the full industrial chain risk standard based on the settlement result and the risk data corresponding to each first-level risk index reflects the overall risk level of the industrial chain. Then, the risk data of each first-level risk index can be compared with the full industrial chain risk standard, and the indicators exceeding or approaching the standard threshold are regarded as high-risk indicators. These indicators are the focus of subsequent risk management and response strategy formulation. At the same time, when formulating strategies, factors such as the magnitude of the risk, the likelihood of occurrence, and the characteristics of the industrial chain should be comprehensively considered to ensure the effectiveness and feasibility of the strategies.

[0142] In the embodiments of the present disclosure, the risk identification method, device, and equipment for the manufacturing industry chain based on the cash flow can more accurately identify potential risks in the manufacturing industry chain by comprehensively using cash flow data and the multi-level risk index judgment matrix, providing strong support for the risk management of the industrial chain, thereby realizing the risk early warning and effective prevention and control of the manufacturing industry chain.

[0143] Those skilled in the art can understand that in the above method of the specific implementation manner, the writing order of each step does not mean a strict execution order and constitutes any limitation to the implementation process. The specific execution order of each step should be determined according to its function and possible internal logic.

[0144] Based on the same inventive concept, the embodiments of the present disclosure also provide a risk identification device for the manufacturing industry chain based on the cash flow corresponding to the risk identification method for the manufacturing industry chain based on the cash flow. Since the principle of solving problems by the device in the embodiments of the present disclosure is similar to the above-mentioned risk identification method for the manufacturing industry chain based on the cash flow in the embodiments of the present disclosure, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be described again.

[0145] Refer to Figure 6 As shown, it is a schematic diagram of a risk identification device 600 for the manufacturing industry chain based on the cash flow provided by the embodiments of the present disclosure. The device includes:

[0146] An index standard determination module 601, configured to obtain a cash flow data set of the manufacturing industry chain; and determine a target evaluation index standard based on a pre-constructed capital risk evaluation standard and the cash flow data set; wherein, the target evaluation index standard includes a plurality of first-level risk indicators, and each first-level risk indicator corresponds to a plurality of second-level risk indicators;

[0147] A judgment matrix construction module 602, configured to construct a first-level risk index judgment matrix based on the plurality of first-level risk indicators; and construct a second-level risk index judgment matrix corresponding to each first-level risk indicator based on the plurality of second-level risk indicators corresponding to each first-level risk indicator;

[0148] A risk data calculation module 603, configured to determine risk data corresponding to each primary risk indicator based on a preset assignment evaluation matrix and the secondary risk indicator judgment matrix corresponding to each primary risk indicator; and determine an entire industrial chain risk standard based on the primary risk indicator judgment matrix and the risk data corresponding to each primary risk indicator.

[0149] A high-risk indicator determination module 604, configured to determine a target high-risk indicator based on the risk data corresponding to each primary risk indicator and the entire industrial chain risk standard.

[0150] In some possible embodiments, the fund flow data set includes fund flow data corresponding to different years; the indicator standard determination module 601 is specifically configured to:

[0151] Obtain fund transaction information regarding the manufacturing industrial chain, and determine a fund risk indicator set based on the fund transaction information; wherein, the fund risk indicator set includes a plurality of primary risk indicators, and each primary risk indicator corresponds to a plurality of secondary risk indicators;

[0152] Determine a to-be-processed fund flow data set based on the fund risk indicator set and the fund flow data set; wherein, the to-be-processed fund flow data set includes fund flow data corresponding to different years for each secondary risk indicator;

[0153] Determine the difference degree value of each secondary risk indicator based on a difference degree formula and the to-be-processed fund flow data set; and calculate the weight of each secondary risk indicator respectively based on a weight calculation formula and the difference degree value of each secondary risk indicator.

[0154] For each secondary risk indicator, calculate the correlation between the secondary risk indicator and other secondary risk indicators respectively; and screen the fund risk indicator set based on the calculation result and the weight of the secondary risk indicator to obtain the target evaluation indicator standard.

[0155] In some possible embodiments, the difference degree formula includes:

[0156]

[0157] wherein, d j represents the difference degree value of the jth secondary risk indicator; m represents the total number of years; P ij represents the normalized fund flow data of the jth secondary risk indicator in the ith year;

[0158] The weight calculation formula includes:

[0159]

[0160] Among them, W j represents the weight of the j-th risk indicator; l represents the number of secondary risk indicators included in the primary risk indicator to which the j-th secondary risk indicator belongs.

[0161] In some possible embodiments, the judgment matrix construction module 602 is specifically configured to:

[0162] Combine the multiple primary risk indicators in pairs respectively to obtain multiple pairs of primary risk indicators;

[0163] Generate a primary indicator importance scoring questionnaire based on the multiple pairs of primary risk indicators, and send the primary indicator importance scoring questionnaire to the receiving terminals corresponding to multiple relevant personnel respectively;

[0164] Receive the scoring results of the primary indicator importance scoring questionnaire by multiple relevant personnel according to the preset assignment scoring rules, and construct a primary risk indicator judgment matrix based on the scoring results of the primary indicator importance scoring questionnaire by the multiple relevant personnel.

[0165] In some possible embodiments, the judgment matrix construction module 602 is specifically configured to:

[0166] For each primary risk indicator, combine the multiple secondary risk indicators corresponding to the primary risk indicator in pairs respectively to obtain multiple pairs of secondary risk indicators;

[0167] Generate a secondary indicator importance scoring questionnaire based on the multiple pairs of secondary risk indicators, and send the secondary indicator importance scoring questionnaire to the receiving terminals corresponding to multiple relevant personnel respectively;

[0168] Receive the scoring results of the secondary indicator importance scoring questionnaire by multiple relevant personnel according to the preset assignment scoring rules, and construct a secondary risk indicator judgment matrix based on the scoring results of the secondary indicator importance scoring questionnaire by the multiple relevant personnel.

[0169] In some possible embodiments, the risk data calculation module 603 is specifically configured to:

[0170] Calculate the risk indicator weights corresponding to each secondary risk indicator judgment matrix, and determine the risk indicator weight matrix corresponding to each primary risk indicator based on the risk indicator weights corresponding to each secondary risk indicator judgment matrix;

[0171] Calculate the score membership degrees corresponding to each secondary risk indicator judgment matrix, and determine the score fuzzy matrix corresponding to each primary risk indicator based on the score membership degrees corresponding to each secondary risk indicator judgment matrix;

[0172] For each first-level risk indicator, risk data corresponding to the first-level risk indicator is determined based on a preset assignment evaluation matrix, the risk indicator weight matrix, and the score fuzzy matrix.

[0173] In some possible embodiments, the risk data calculation module 603 is specifically configured to:

[0174] Calculate the risk indicator weights corresponding to the first-level risk indicator judgment matrix, and determine the full industrial chain risk standard based on the settlement result and the risk data corresponding to each first-level risk indicator.

[0175] Based on the same inventive concept, an embodiment of the present disclosure also provides a computer device. Referring to Figure 7 As shown, it is a schematic structural diagram of a computer device 700 provided by an embodiment of the present disclosure, including a processor 701, a memory 702, and a bus 703. Among them, the memory 702 is used to store execution instructions, including an internal memory 7021 and an external memory 7022; the internal memory 7021 here is also called the main memory, which is used to temporarily store the operation data in the processor 701 and the data exchanged with the external memory 7022 such as a hard disk. The processor 701 exchanges data with the external memory 7022 through the internal memory 7021.

[0176] In an embodiment of the present application, the memory 702 is specifically used to store the application program code for implementing the solution of the present application, and is controlled by the processor 701 to execute. That is, when the computer device 700 runs, the processor 701 communicates with the memory 702 through the bus 703, so that the processor 701 executes the application program code stored in the memory 702, and further executes the method described in any of the foregoing embodiments.

[0177] Among them, the memory 702 may be, but is not limited to, a random access memory (RAM), a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), etc.

[0178] The processor 701 may be an integrated circuit chip with the ability to process signals. The above-mentioned processor may be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. It can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0179] It can be understood that the structure schematically shown in the embodiments of this application does not constitute a specific limitation on the computer device 700. In other embodiments of this application, the computer device 700 may include more or fewer components than those shown in the figure, or combine certain components, or split certain components, or have different component arrangements. The components shown in the figure can be implemented in hardware, software, or a combination of software and hardware.

[0180] The embodiments of the present disclosure also provide a computer-readable storage medium, on which a computer program is stored. When the computer program is run by a processor, it executes the steps of the risk identification method for the manufacturing industry chain based on the cash flow described in the above method embodiments. Among them, the storage medium may be a volatile or non-volatile computer-readable storage medium.

[0181] The embodiments of the present disclosure also provide a computer program product, which carries program codes. The instructions included in the program codes can be used to execute the steps of the risk identification method for the manufacturing industry chain based on the cash flow described in the above method embodiments. For specific details, please refer to the above method embodiments and will not be elaborated here.

[0182] Among them, the above computer program product can be specifically implemented in a manner of hardware, software, or a combination thereof. In an alternative embodiment, the computer program product is specifically embodied as a computer storage medium. In another alternative embodiment, the computer program product is specifically embodied as a software product, such as a Software Development Kit (SDK), etc.

[0183] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the systems and devices described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein. In several embodiments provided in the present disclosure, it should be understood that the disclosed systems and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For another example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some communication interfaces, and the indirect couplings or communication connections of the devices or units can be in electrical, mechanical or other forms.

[0184] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0185] In addition, in each embodiment of the present disclosure, the functional units can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit.

[0186] If the functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium executable by a processor. Based on such an understanding, the technical solution of the present disclosure, in essence, or the part that contributes to the prior art or a part of this technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present disclosure. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical discs that can store program codes.

[0187] Finally, it should be noted that the above-described embodiments are only specific embodiments of the present disclosure, which are used to illustrate the technical solutions of the present disclosure, rather than to limit them. The protection scope of the present disclosure is not limited thereto. Although the present disclosure has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that any person skilled in the art within the technical scope disclosed by the present disclosure can still modify the technical solutions described in the foregoing embodiments, or can easily think of changes, or make equivalent replacements for some of the technical features; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present disclosure, and should all be covered within the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be subject to the protection scope of the claims.

Claims

1. A risk identification method for the manufacturing industry chain based on capital flow, characterized in that: include: Obtain the capital flow dataset of the manufacturing industry chain; and determining a target evaluation index standard based on the pre-constructed capital risk evaluation standard and the capital flow data set; wherein the target evaluation index standard includes a plurality of primary risk indicators, and each primary risk indicator corresponds to a plurality of secondary risk indicators; Constructing a first-level risk indicator judgment matrix based on the multiple first-level risk indicators; and constructing a second-level risk indicator judgment matrix corresponding to each first-level risk indicator based on the multiple second-level risk indicators corresponding to each first-level risk indicator; Determine the risk data corresponding to each first-level risk indicator based on the preset value evaluation matrix and the second-level risk indicator judgment matrix corresponding to each first-level risk indicator; and determine the risk standard of the entire industry chain based on the first-level risk indicator judgment matrix and the risk data corresponding to each first-level risk indicator; The target high-risk indicator is determined based on the risk data corresponding to each first-level risk indicator and the risk standard of the entire industry chain.

2. The method according to claim 1, characterized in that The fund flow data set includes fund flow data corresponding to different years; the target evaluation index standard determined based on the pre-constructed fund risk evaluation standard and the fund flow data set includes: Acquire fund transaction information about the manufacturing industry chain, and determine a fund risk indicator set based on the fund transaction information; wherein the fund risk indicator set includes a plurality of primary risk indicators, and each primary risk indicator corresponds to a plurality of secondary risk indicators; Determining a to-be-processed fund flow data set based on the fund risk indicator set and the fund flow data set; wherein the to-be-processed fund flow data set includes fund flow data of different years corresponding to each secondary risk indicator; Determine the difference value of each secondary risk indicator based on the difference formula and the fund flow data set to be processed; and calculate the weight of each secondary risk indicator based on the weight calculation formula and the difference value of each secondary risk indicator; For each secondary risk indicator, the correlation between the secondary risk indicator and other secondary risk indicators is calculated respectively; and based on the calculation result and the weight of the secondary risk indicator, the fund risk indicator set is screened to obtain the target evaluation indicator standard.

3. The method according to claim 2, characterized in that The difference formula includes: Among them, d j It is represented by the difference value of the j-th secondary risk indicator; m is represented by the total number of years; P ij It is represented by the normalized fund flow data of the jth secondary risk indicator in the i-th year; The weight calculation formula includes: Among them, W j It is represented as the weight of the j-th risk indicator; l is represented as the number of secondary risk indicators contained in the primary risk indicator to which the j-th secondary risk indicator belongs.

4. The method according to claim 2, characterized in that: The constructing a first-level risk indicator judgment matrix based on the multiple first-level risk indicators includes: Combining the plurality of first-level risk indicators in pairs respectively to obtain a plurality of first-level risk indicator pairs; Generate a first-level indicator importance scoring questionnaire based on the multiple first-level risk indicators, and send the first-level indicator importance scoring questionnaire to receiving terminals corresponding to multiple relevant personnel respectively; Receive the scoring results of the first-level indicator importance scoring questionnaire from multiple relevant personnel according to the preset assignment scoring rules, and construct a first-level risk indicator judgment matrix based on the scoring results of the first-level indicator importance scoring questionnaire from the multiple relevant personnel.

5. The method according to claim 4, characterized in that The step of constructing a secondary risk indicator judgment matrix corresponding to each primary risk indicator based on the multiple secondary risk indicators corresponding to each primary risk indicator includes: For each primary risk indicator, multiple secondary risk indicators corresponding to the primary risk indicator are combined in pairs to obtain multiple secondary risk indicator pairs; Generate a secondary indicator importance scoring questionnaire based on the multiple secondary risk indicators, and send the secondary indicator importance scoring questionnaire to receiving terminals corresponding to multiple relevant personnel respectively; Receive the scoring results of the secondary indicator importance scoring questionnaire from multiple relevant personnel according to the preset assignment scoring rules, and construct a secondary risk indicator judgment matrix based on the scoring results of the secondary indicator importance scoring questionnaire from the multiple relevant personnel.

6. The method according to claim 5, characterized in that The step of determining the risk data corresponding to each primary risk indicator based on the preset value evaluation matrix and the secondary risk indicator judgment matrix corresponding to each primary risk indicator includes: Calculating the risk indicator weight corresponding to each of the secondary risk indicator judgment matrices, and determining the risk indicator weight matrix corresponding to each primary risk indicator based on the risk indicator weight corresponding to each of the secondary risk indicator judgment matrices; Calculating the score membership corresponding to each of the secondary risk indicator judgment matrices, and determining the score fuzzy matrix corresponding to each primary risk indicator based on the score membership corresponding to each of the secondary risk indicator judgment matrices; For each primary risk indicator, the risk data corresponding to the primary risk indicator is determined based on a preset value assignment evaluation matrix, the risk indicator weight matrix and the score fuzzy matrix.

7. The method according to claim 6, characterized in that The determining of the risk standard of the entire industry chain based on the first-level risk indicator judgment matrix and the risk data corresponding to each first-level risk indicator includes: The risk indicator weights corresponding to the first-level risk indicator judgment matrix are calculated, and the risk standard of the entire industry chain is determined based on the settlement results and the risk data corresponding to each first-level risk indicator.

8. A risk identification device for a manufacturing industry chain based on capital flow, characterized in that: include: The indicator standard determination module is used to obtain the capital flow data set of the manufacturing industry chain; and determining a target evaluation index standard based on the pre-constructed capital risk evaluation standard and the capital flow data set; wherein the target evaluation index standard includes a plurality of primary risk indicators, and each primary risk indicator corresponds to a plurality of secondary risk indicators; A judgment matrix construction module, used to construct a first-level risk indicator judgment matrix based on the multiple first-level risk indicators; and to construct a second-level risk indicator judgment matrix corresponding to each first-level risk indicator based on the multiple second-level risk indicators corresponding to each first-level risk indicator; A risk data calculation module, for determining the risk data corresponding to each primary risk indicator based on a preset value evaluation matrix and the secondary risk indicator judgment matrix corresponding to each primary risk indicator; and determining the risk standard of the entire industry chain based on the primary risk indicator judgment matrix and the risk data corresponding to each primary risk indicator; The high-risk indicator determination module is used to determine the target high-risk indicator based on the risk data corresponding to each first-level risk indicator and the risk standard of the entire industrial chain.

9. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

10. A computer device comprising a storage medium, a processor, and a computer program stored in the storage medium and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

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