Data asset value evaluation system and method based on system dynamics

Abstract

The invention discloses a data asset value evaluation system and method based on system dynamics. The system comprises a data asset evaluation data acquisition module; a data asset value evaluation cost subsystem module; the data asset value evaluation data attribute subsystem module is used for calculating a data attribute adjustment coefficient; the data asset value evaluation economic subsystem module is used for calculating an economic factor adjustment coefficient; the data asset value assessment risk subsystem module is used for calculating a risk factor adjustment coefficient; and the data asset value evaluation system module is used for evaluating the data asset value according to the data asset cost, the data attribute adjustment coefficient, the economic factor adjustment coefficient and the risk factor adjustment coefficient. According to the method, through data asset value quantification, the requirements of various industries or fields for data asset value management can be met, reference is provided for data asset value evaluation, and meanwhile a foundation is laid for promoting data asset transaction and improving the data operation level.

Description

Technical Field

[0001] The present invention relates to the field of data asset value, and more specifically, to a data asset value assessment system and method based on system dynamics. Background Art

[0002] The technological driving force for the development of the digital economy is shifting from process-driven to data-driven. Data has become a key production factor, and data assets have become an important resource for building a digital China. The in-depth promotion of the marketization of data elements has put forward higher requirements for the value assessment of data assets.

[0003] However, traditional statistical methods assume that the various factors affecting the value of data assets are independent of each other. Indeed, the various factors affecting the value of data assets are interrelated rather than independent, and the formation of their value is a complex system process. Therefore, a data asset value assessment system and method based on system dynamics is constructed to help meet the needs of various industries or fields for data asset value management, provide a reference for their data asset value assessment, and also lay the foundation for promoting data asset transactions and improving data operation levels. Summary of the invention

[0004] In order to solve the above problems, the present invention provides a data asset value assessment system based on system dynamics, characterized in that the system comprises:

[0005] Data asset assessment data acquisition module, used to obtain relevant data of data assets;

[0006] Data asset value assessment cost subsystem module, used to calculate data asset costs;

[0007] Data asset value assessment data attribute subsystem module, used to calculate data attribute adjustment coefficients;

[0008] The economic subsystem module for data asset value assessment is used to calculate the adjustment coefficient of economic factors;

[0009] Data asset value assessment risk subsystem module, used to calculate risk factor adjustment coefficients;

[0010] The data asset value assessment system module is used to assess the data asset value based on the data asset cost, the data attribute adjustment coefficient, the economic factor adjustment coefficient and the risk factor adjustment coefficient.

[0011] Optionally, in one embodiment of the present application, the data asset value assessment is based on the following model to assess the data asset value:

[0012] Data asset value = INTGE (increment in assetization - depreciation, α),

[0013] Among them, α is the initial value.

[0014] Optionally, in one embodiment of the present application, the calculation formula for the assetization increment is:

[0015] Assetization increment = data asset cost * (1 + data attribute adjustment coefficient + economic factor adjustment coefficient - risk factor adjustment coefficient),

[0016] The data attribute adjustment coefficient is A1*data attribute, A1 is the weight corresponding to the data attribute in the assetization increment, the economic factor adjustment coefficient is A2*economic factor, A2 is the weight corresponding to the economic factor in the assetization increment, and the risk factor adjustment coefficient is A3*risk factor, A3 is the weight corresponding to the risk factor in the assetization increment. And 0≤A i ≤1.

[0017] Optionally, in one embodiment of the present application, the calculation formula of the depreciation amount is:

[0018] Depreciation amount = data asset value * depreciation rate,

[0019] Wherein, the depreciation rate = B1 / the economic factor + B2 / technical level, B1 is the weight corresponding to the economic factor in the depreciation rate, B2 is the weight corresponding to the technical level in the depreciation rate, And 0≤B i ≤1.

[0020] Optionally, in one embodiment of the present application, the cost subsystem to which the data asset cost belongs includes three auxiliary variables: labor input, equipment and technology input, and annual cost input. The data asset cost calculation formula is:

[0021] Annual cost input = labor input + equipment and technology input,

[0022] Among them, the labor input is WITH LOOKUP (Time), and the equipment and technology input is WITH LOOKUP (Time).

[0023] Optionally, in one embodiment of the present application, the data attribute subsystem to which the data attribute belongs includes 1 horizontal variable: scale, 2 rate variables: mining volume, rejection volume, 6 auxiliary variables: data scale conversion rate, original data volume, data resources, accuracy, timeliness, data attributes, 6 constants: effective utilization rate, mining ratio, coverage times, completeness, rejection ratio, technical level, and manager quality. The data attribute calculation formula is:

[0024] Data attributes = C1*accuracy + C2*completeness + C3*data scale conversion rate + C4*timeliness,

[0025] Among them, C1 is the weight corresponding to the accuracy in the data attribute, C2 is the weight corresponding to the completeness in the data attribute, C3 is the weight corresponding to the scale in the data attribute, and C4 is the weight corresponding to the timeliness in the data attribute. And 0≤C i ≤1;

[0026] The accuracy is calculated as:

[0027] Accuracy = D1*manager quality + D2*technical level,

[0028] Among them, D1 is the weight corresponding to the manager's quality in data accuracy, and D2 is the weight corresponding to the technical level in data accuracy. And 0≤D i ≤1;

[0029] The calculation formula of the data scale conversion rate is:

[0030] Data scale conversion rate = scale / data resources,

[0031] Wherein, the scalability = INTGE (the mining volume - the rejection volume, β), β is the initial value, the mining volume = the number of coverage times * the effective utilization rate * the mining ratio * the data resource / the original data volume, the rejection volume = the scalability * the rejection ratio, the original data volume = WITH LOOKUP (Time), the data resource = E1 * the original data volume, E1 is the weight corresponding to the original data in the data resource, and 0≤E1≤1;

[0032] The calculation formula for timeliness is:

[0033] Timeliness = F1*manager quality + F2*technical level,

[0034] Among them, F1 represents the weight corresponding to the manager's quality in the data timeliness, and F2 represents the weight corresponding to the technical level in the data timeliness. And 0≤F i ≤1.

[0035] Optionally, in one embodiment of the present application, the economic subsystem to which the economic factors belong includes 7 auxiliary variables: relevance, profitability, peer profitability, substitutability, scarcity, scenario adaptability, economic factors, and 3 constants: market competition level, technology level, and data scale conversion rate. The economic factor calculation formula is:

[0036] Economic factors = G1 relevance* + G2* scarcity + G3* scenario adaptability + G4* substitutability,

[0037] Among them, G1 represents the weight corresponding to the correlation in the economic factors; G2 represents the weight corresponding to the scarcity in the economic factors; G3 represents the weight corresponding to the scene adaptability in the economic factors; G4 represents the weight corresponding to the substitutability in the economic factors. And 0≤G i ≤1;

[0038] The calculation formula of the correlation is:

[0039] Relevance = H1*data scale conversion rate,

[0040] Wherein, H1 represents the weight corresponding to the data scale conversion rate in data relevance, and 0≤H i ≤1;

[0041] The calculation formula of the scarcity is:

[0042] Scarcity = I1*market competition level,

[0043] Where I1 represents the weight corresponding to the market competition degree in data scarcity, and 0≤I i ≤1;

[0044] The calculation formula of the scene adaptability is:

[0045] Scenario adaptability = IF THEN ELSE (profitability >= profitability of peers, 1, profitability / profitability of peers), where the profitability is WITH LOOKUP (Time), and the profitability of peers is WITH LOOKUP (Time);

[0046] The calculation formula of the substitutability is:

[0047] Substitutability = J1*market competition level + J2 / technical level,

[0048] Among them, J1 represents the weight corresponding to the degree of market competition in data substitutability, and J2 represents the weight corresponding to the technical level in data substitutability. And 0≤J i ≤1.

[0049] Optionally, in one embodiment of the present application, the economic subsystem to which the risk factor belongs includes five auxiliary variables: value-added development risk, data security risk, management risk, circulation risk, risk factor, and three constants: manager quality, financial risk level, and technical level. The risk factor calculation formula is:

[0050] Risk factors = K1 value-added development risk* + K2* data security risk + K3* management risk + K4* circulation risk,

[0051] Among them, K1 represents the weight corresponding to the circulation risk in the risk factors; K2 represents the weight corresponding to the data security risk in the risk factors; K3 represents the weight corresponding to the management risk in the risk factors; K4 represents the weight corresponding to the value-added development risk in the risk factors. And 0≤K i ≤1;

[0052] The calculation formula for the value-added development risk is:

[0053] Value-added development risk = L1 / technology level,

[0054] Where L1 represents the weight corresponding to the technical level in the value-added development risk, and 0≤L i ≤1;

[0055] The calculation formula for the data security risk is:

[0056] Data security risk = M1 / technical level,

[0057] Wherein, M1 represents the weight corresponding to the technical level in the data security risk, and 0≤M i ≤1;

[0058] The calculation formula for the management risk is:

[0059] Management risk = N1* manager quality + N2* financial risk level,

[0060] Among them, N1 represents the weight corresponding to the manager's quality in the management risk; N2 represents the weight corresponding to the financial risk level in the management risk. And 0≤N i ≤1;

[0061] The calculation formula of the circulation risk is:

[0062] Circulation risk = O1 data security risk* + O2* management risk + O3* value-added development risk,

[0063] Among them, O1 represents the weight corresponding to the data security risk in the circulation risk; O2 represents the weight corresponding to the management risk in the circulation risk; O3 represents the weight corresponding to the value-added development risk in the circulation risk. And 0≤O i ≤1.

[0064] In addition, the present invention also discloses a data asset value assessment method based on system dynamics, the method comprising the following steps:

[0065] Obtain relevant data about data assets;

[0066] Calculate the cost of data assets;

[0067] Calculate data attribute adjustment coefficients;

[0068] Calculate the economic adjustment factor;

[0069] Calculate risk factor adjustment coefficients;

[0070] The data asset value is evaluated based on the data asset cost, the data attribute adjustment coefficient, the economic factor adjustment coefficient and the risk factor adjustment coefficient.

[0071] In response to the existing problems, the present invention provides a data asset value assessment system and method based on system dynamics to solve the problem of not being able to fully consider the mutual and comprehensive influence of multiple factors such as data attributes, economy, and risk. By constructing a data asset value assessment system based on system dynamics, the mutual influence of cost, data attributes, economy, and risk factors is analyzed to quantify the value of data assets. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of the present invention, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

[0073] Figure 1 A diagram showing the structure of a data asset value assessment system based on system dynamics provided by the present invention;

[0074] Figure 2 A schematic diagram of the cause-effect relationship of the data asset value system provided by the present invention;

[0075] Figure 3 A schematic diagram of the data asset value assessment system model provided by the present invention;

[0076] Figure 4A schematic diagram of the data asset value assessment cost subsystem provided by the present invention;

[0077] Figure 5 A schematic diagram of a data attribute subsystem for data asset value assessment provided by the present invention;

[0078] Figure 6 A schematic diagram of the economic subsystem for data asset value assessment provided by the present invention;

[0079] Figure 7 Schematic diagram of the data asset value assessment risk subsystem provided by the present invention. DETAILED DESCRIPTION

[0080] In order to make the objectives, technical solutions and advantages of the present application more clearly understood, the present invention is further described in detail below in conjunction with embodiments and drawings. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0081] The present invention provides a data asset value assessment system and method based on system dynamics, see Figure 1 As shown, Figure 1 The present invention provides a structure diagram of the data asset value assessment system based on system dynamics, and the system includes: a data asset assessment data acquisition module, a data asset value assessment cost subsystem module, a data asset value assessment data attribute subsystem module, a data asset value assessment economic subsystem module, a data asset value assessment risk subsystem module, and a data asset value assessment system module.

[0082] Among them, the data asset evaluation data acquisition module is used to obtain relevant data of data assets;

[0083] Data asset value assessment cost subsystem module, used to calculate data asset costs;

[0084] Data asset value assessment data attribute subsystem module, used to calculate data attribute adjustment coefficients;

[0085] The economic subsystem module for data asset value assessment is used to calculate the adjustment coefficient of economic factors;

[0086] Data asset value assessment risk subsystem module, used to calculate risk factor adjustment coefficients;

[0087] The data asset value assessment system module is used to assess the value of data assets.

[0088] In one embodiment, Figure 2 and Figure 3As shown in the figure, the variables directly related to the data asset value in the data asset value assessment system module include: 1 horizontal variable: data asset value, 2 rate variables: assetization increment and depreciation, 5 auxiliary variables: depreciation rate, cost input, data attributes, economic factors, and risk factors, and 1 constant: technical level. The specific selection of each variable is shown in Table 1-1.

[0089] Table 1-1 Summary of variables directly related to the value of data assets

[0090] Serial number Indicator name Variable Types unit 1 Data asset value Level variables Dimensionless 2 Incremental capitalization Rate Variable Dimensionless 3 Depreciation Rate Variable Dimensionless 4 Depreciation rate Auxiliary variables Dimensionless 5 Cost input Auxiliary variables Dimensionless 6 Data attributes Auxiliary variables Dimensionless 7 Economic factors Auxiliary variables Dimensionless 8 Risk factors Auxiliary variables Dimensionless 9 Technical Level constant Dimensionless

[0091] The data asset value assessment system module assesses the value of data assets based on the following model:

[0092] Data asset value = INTGE (increment in assetization - depreciation, α),

[0093] Among them, α is the initial value.

[0094] The parameter α in the above model can be determined by one of the following methods:

[0095] (1) Subjective determination method. Experts can score or customers can specify specific parameter values. In one embodiment of the present invention, we set α to 1.8;

[0096] (2) Objective determination method. Refer to the initial value of the data asset value in the comparable data asset value of the same type and the same purpose in the market, and use these data as input, train and learn, and use parameter optimization methods such as the coefficient of variation method to determine the specific value of the parameter;

[0097] (3) Combination of subjective and objective methods: Determine by combining the above two methods.

[0098] The capitalization increment is calculated based on the following formula:

[0099] Assetization increment = data asset cost * (1 + data attribute adjustment coefficient + economic factor adjustment coefficient - risk factor adjustment coefficient),

[0100] Among them, the data attribute adjustment coefficient is A1*data attribute, A1 is the weight corresponding to the data attribute in the assetization increment, the economic factor adjustment coefficient is A2*economic factor, A2 is the weight corresponding to the economic factor in the assetization increment, and the risk factor adjustment coefficient is A3*risk factor, A3 is the weight corresponding to the risk factor in the assetization increment. And 0≤A i ≤1.

[0101] The depreciation amount is calculated based on the following formula:

[0102] Depreciation amount = data asset value * depreciation rate,

[0103] Among them, depreciation rate = B1 / economic factors + B2 / technical level, B1 is the weight corresponding to the economic factors in the depreciation rate, B2 is the weight corresponding to the technical level in the depreciation rate, And 0≤B i ≤1.

[0104] In the data asset value assessment cost subsystem module, such as Figure 4 As shown, the variables directly related to data asset costs include three auxiliary variables: labor input, equipment and technology input, and annual cost input. The specific selection of each variable is shown in Table 1-2.

[0105] Table 1-2 Summary of cost subsystem variables

[0106] Serial number Indicator name Variable Types unit 1 Equipment and technology investment Auxiliary variables Ten thousand yuan 2 Labor input Auxiliary variables Ten thousand yuan 3 Annual cost input Auxiliary variables Ten thousand yuan

[0107] The data asset value assessment cost subsystem module calculates the data asset cost based on the following formula:

[0108] Annual cost input = labor input + equipment and technology input,

[0109] Among them, labor input is WITH LOOKUP (Time), and equipment and technology input is WITH LOOKUP (Time).

[0110] In the data asset value assessment data attribute subsystem module, such as Figure 5 As shown, the variables directly related to data attributes include 1 horizontal variable: scale, 2 rate variables: mining volume and elimination volume, 6 auxiliary variables: data scale conversion rate, original data volume, data resources, accuracy, timeliness, and data attributes, and 6 constants: effective utilization rate, mining ratio, coverage times, completeness, elimination ratio, technical level, and manager quality. The specific selection of each variable is shown in Table 1-3.

[0111] Table 1-3 Summary of data attribute subsystem variables

[0112]

[0113]

[0114] The data attribute subsystem module for data asset value assessment calculates data attributes based on the following formula:

[0115] Data attributes = C1*accuracy + C2*completeness + C3*data scale conversion rate + C4*timeliness,

[0116] Among them, C1 is the weight corresponding to accuracy in the data attribute, C2 is the weight corresponding to completeness in the data attribute, C3 is the weight corresponding to scale in the data attribute, and C4 is the weight corresponding to timeliness in the data attribute. And 0≤C i ≤1.

[0117] The accuracy is calculated as:

[0118] Accuracy = D1*manager quality + D2*technical level,

[0119] Among them, D1 is the weight corresponding to the quality of managers in data accuracy; D2 is the weight corresponding to the technical level in data accuracy. And 0≤D i ≤1.

[0120] The calculation formula for data scale conversion rate is:

[0121] Data scale conversion rate = scale / data resources,

[0122] Among them, scalability = INTGE (mining volume - elimination volume, β), β is the initial value, mining volume = coverage times * effective utilization rate * mining ratio * data resources / original data volume, elimination volume = scalability * elimination ratio, original data volume = WITHLOOKUP (Time), data resources = E1 * original data volume, E1 is the weight corresponding to the original data in the data resources, and 0≤E1≤1.

[0123] The parameter β can be determined by one of the following methods:

[0124] (1) Subjective determination method. Experts can score or customers can specify specific parameter values. In one embodiment of the present invention, we set β to 2.4;

[0125] (2) Objective determination method. Refer to the initial value of the data asset value in the comparable data asset value of the same type and the same purpose in the market, and use these data as input, train and learn, and use parameter optimization methods such as the coefficient of variation method to determine the specific value of the parameter;

[0126] (3) Combination of subjective and objective methods: Determine by combining the above two methods.

[0127] The timeliness calculation formula is:

[0128] Timeliness = F1*manager quality + F2*technical level,

[0129] Among them, F1 represents the weight corresponding to the quality of managers in data timeliness, and F2 represents the weight corresponding to the technical level in data timeliness. And 0≤F i ≤1.

[0130] In the data asset value assessment economic subsystem module, such as Figure 6 As shown in Table 1-4, the variables directly related to economic factors include 7 auxiliary variables: relevance, profitability, peer profitability, substitutability, scarcity, scenario adaptability, and economic factors, and 3 constants: market competition level, technological level, and data scale conversion rate. The specific selection of each variable is shown in Table 1-4.

[0131] Table 1-4 Summary of variables in the economic subsystem

[0132] Serial number Indicator name Variable Types unit 1 Relevance Auxiliary variables Dimensionless 2 Profitability Auxiliary variables Dimensionless 3 Peer profitability Auxiliary variables Dimensionless 4 Substitutability Auxiliary variables Dimensionless 5 Scarcity Auxiliary variables Dimensionless 6 Scene adaptability Auxiliary variables Dimensionless 7 Economic factors Auxiliary variables Dimensionless 8 Market competition constant Dimensionless 9 Technical Level constant Dimensionless 10 Data scale conversion rate constant Dimensionless

[0133] The economic subsystem module for data asset value assessment calculates economic factors based on the following formula:

[0134] Economic factors = G1 relevance* + G2* scarcity + G3* scenario adaptability + G4* substitutability,

[0135] Among them, G1 represents the weight corresponding to relevance in economic factors; G2 represents the weight corresponding to scarcity in economic factors; G3 represents the weight corresponding to scenario adaptability in economic factors; G4 represents the weight corresponding to substitutability in economic factors. And 0≤G i ≤1.

[0136] The formula for calculating the correlation is:

[0137] Relevance = H1*data scale conversion rate,

[0138] Among them, H1 represents the weight corresponding to the data scale conversion rate in data relevance, and 0≤H i ≤1.

[0139] The formula for calculating scarcity is:

[0140] Scarcity = I1*market competition level,

[0141] Among them, I1 represents the weight corresponding to the degree of market competition in data scarcity, and 0≤I i ≤1.

[0142] The calculation formula for scene adaptability is:

[0143] Scenario adaptability = IF THEN ELSE (profitability >= profitability of peers, 1, profitability / profitability of peers),

[0144] Among them, profitability is WITH LOOKUP(Time) and peer profitability is WITH LOOKUP(Time).

[0145] The formula for calculating substitutability is:

[0146] Substitutability = J1*market competition level + J2 / technical level,

[0147] Among them, J1 represents the weight corresponding to the degree of market competition in data substitutability; J2 represents the weight corresponding to the technical level in data substitutability. And 0≤J i ≤1.

[0148] In the data asset value assessment risk subsystem module, such as Figure 7 As shown, the variables directly related to risk factors include 5 auxiliary variables: value-added development risk, data security risk, management risk, circulation risk, and risk factors, and 3 constants: manager quality, financial risk level, and technical level. The specific selection of each variable is shown in Table 1-5.

[0149] Table 1-5 Summary of risk subsystem variables

[0150] Serial number Indicator name Variable Types unit 1 Value-added development risk Auxiliary variables Dimensionless 2 Data security risks Auxiliary variables Dimensionless 3 Managing Risk Auxiliary variables Dimensionless 4 Circulation risk Auxiliary variables Dimensionless 5 Risk factors Auxiliary variables Dimensionless 6 Manager quality constant Dimensionless 7 Financial risk level constant Dimensionless 8 Technical Level constant Dimensionless

[0151] The data asset value assessment risk subsystem module calculates risk factors based on the following formula:

[0152] Risk factors = K1 value-added development risk* + K2* data security risk + K3* management risk + K4* circulation risk,

[0153] Among them, K1 represents the weight corresponding to the circulation risk in the risk factors; K2 represents the weight corresponding to the data security risk in the risk factors; K3 represents the weight corresponding to the management risk in the risk factors; K4 represents the weight corresponding to the value-added development risk in the risk factors. And 0≤K i ≤1.

[0154] The formula for calculating the value-added development risk is:

[0155] Value-added development risk = L1 / technology level,

[0156] Among them, L1 represents the weight corresponding to the technology level in the value-added development risk, and 0≤L i ≤1.

[0157] The calculation formula for data security risk is:

[0158] Data security risk = M1 / technical level,

[0159] Among them, M1 represents the weight corresponding to the technical level in data security risk, and 0≤M i ≤1.

[0160] The calculation formula for management risk is:

[0161] Management risk = N1* manager quality + N2* financial risk level,

[0162] Among them, N1 represents the weight corresponding to the quality of managers in the management risk; N2 represents the weight corresponding to the financial risk level in the management risk. And 0≤N i ≤1.

[0163] The calculation formula for circulation risk is:

[0164] Circulation risk = O1 data security risk* + O2* management risk + O3* value-added development risk,

[0165] Among them, O1 represents the weight corresponding to data security risk in circulation risk; O2 represents the weight corresponding to management risk in circulation risk; O3 represents the weight corresponding to value-added development risk in circulation risk. And 0≤O i ≤1.

[0166] Compared with the prior art, the present invention has the following beneficial effects:

[0167] (1) The present invention provides a data asset value assessment system based on system dynamics. Existing data asset value assessment models cannot take into account the interrelationships between various factors. The present invention constructs a data asset value assessment system based on system dynamics, which comprehensively considers the interrelationships between various factors and can improve the accuracy of the data asset value assessment model to a certain extent.

[0168] (2) The present invention provides a method for quantifying the value of data assets. Due to the characteristics of data assets, existing intangible asset evaluation methods are not suitable for evaluating the value of data assets. The data asset value evaluation system and method based on system dynamics proposed in the present invention can select subjective, objective or a combination of subjective and objective methods to determine parameters according to actual conditions to achieve data asset quantification.

[0169] Although specific embodiments of the present invention have been shown and described, it is apparent to those skilled in the art that changes and modifications may be made based on the teachings herein without departing from the exemplary embodiments of the present invention and its broader aspects. Therefore, the appended claims are intended to include within their scope all such changes and modifications that do not depart from the true spirit and scope of the exemplary embodiments of the present invention.

Claims

1. A data asset value assessment system based on system dynamics, characterized in that: The system comprises: Data asset assessment data acquisition module, used to obtain relevant data of data assets; Data asset value assessment cost subsystem module, used to calculate data asset costs; Data asset value assessment data attribute subsystem module, used to calculate data attribute adjustment coefficients; The economic subsystem module for data asset value assessment is used to calculate the adjustment coefficient of economic factors; Data asset value assessment risk subsystem module, used to calculate risk factor adjustment coefficients; The data asset value assessment system module is used to assess the data asset value based on the data asset cost, the data attribute adjustment coefficient, the economic factor adjustment coefficient and the risk factor adjustment coefficient.

2. A data asset value assessment system based on system dynamics according to claim 1, characterized in that: The data asset value assessment is based on the following model to assess the value of data assets: Data asset value = INTGE (increment in assetization - depreciation, α), Among them, α is the initial value.

3. A data asset value assessment system based on system dynamics according to claim 2, characterized in that: The calculation formula for the assetization increment is: Assetization increment = data asset cost * (1 + data attribute adjustment coefficient + economic factor adjustment coefficient - risk factor adjustment coefficient), The data attribute adjustment coefficient is A1*data attribute, A1 is the weight corresponding to the data attribute in the assetization increment, the economic factor adjustment coefficient is A2*economic factor, A2 is the weight corresponding to the economic factor in the assetization increment, and the risk factor adjustment coefficient is A3*risk factor, A3 is the weight corresponding to the risk factor in the assetization increment. And 0≤A i ≤1.

4. A data asset value assessment system based on system dynamics according to claim 2, characterized in that: The calculation formula of the depreciation amount is: Depreciation amount = data asset value * depreciation rate, Wherein, the depreciation rate = B1 / the economic factor + B2 / technical level, B1 is the weight corresponding to the economic factor in the depreciation rate, B2 is the weight corresponding to the technical level in the depreciation rate, And 0≤B i ≤1.

5. A data asset value assessment system based on system dynamics according to claim 3, characterized in that: The cost subsystem to which the data asset cost belongs includes three auxiliary variables: labor input, equipment and technology input, and annual cost input. The data asset cost calculation formula is: Annual cost input = labor input + equipment and technology input, Among them, the labor input is WITH LOOKUP (Time), and the equipment and technology input is WITH LOOKUP (Time).

6. A data asset value assessment system based on system dynamics according to claim 3, characterized in that: The data attribute subsystem to which the data attribute belongs includes 1 horizontal variable: scale, 2 rate variables: mining volume, elimination volume, 6 auxiliary variables: data scale conversion rate, original data volume, data resources, accuracy, timeliness, data attributes, 6 constants: effective utilization rate, mining ratio, coverage times, completeness, elimination ratio, technical level, and manager quality. The data attribute calculation formula is: Data attributes = C1*accuracy + C2*completeness + C3*data scale conversion rate + C4*timeliness, Among them, C1 is the weight corresponding to the accuracy in the data attribute, C2 is the weight corresponding to the completeness in the data attribute, C3 is the weight corresponding to the scale in the data attribute, and C4 is the weight corresponding to the timeliness in the data attribute. And 0≤C i ≤1; The accuracy is calculated as: Accuracy = D1*manager quality + D2*technical level, Among them, D1 is the weight corresponding to the manager's quality in data accuracy, and D2 is the weight corresponding to the technical level in data accuracy. And 0≤D i ≤1; The calculation formula of the data scale conversion rate is: Data scale conversion rate = scale / data resources, Wherein, the scalability = INTGE (the mining volume - the rejection volume, β), β is the initial value, the mining volume = the number of coverage times * the effective utilization rate * the mining ratio * the data resource / the original data volume, the rejection volume = the scalability * the rejection ratio, the original data volume = WITH LOOKUP (Time), the data resource = E1 * the original data volume, E1 is the weight corresponding to the original data in the data resource, and 0≤E1≤1; The calculation formula for timeliness is: Timeliness = F1*manager quality + F2*technical level, Among them, F1 represents the weight corresponding to the manager's quality in the data timeliness, and F2 represents the weight corresponding to the technical level in the data timeliness. And 0≤F i ≤1.

7. A data asset value assessment system based on system dynamics according to claim 3, characterized in that: The economic subsystem to which the economic factors belong includes 7 auxiliary variables: relevance, profitability, peer profitability, substitutability, scarcity, scenario adaptability, economic factors, and 3 constants: market competition level, technology level, and data scale conversion rate. The economic factor calculation formula is: Economic factors = G1 relevance* + G2* scarcity + G3* scenario adaptability + G4* substitutability, Among them, G1 represents the weight corresponding to the correlation in the economic factors; G2 represents the weight corresponding to the scarcity in the economic factors; G3 represents the weight corresponding to the scene adaptability in the economic factors; G4 represents the weight corresponding to the substitutability in the economic factors. And 0≤G i ≤1; The calculation formula of the correlation is: Relevance = H1*data scale conversion rate, Wherein, H1 represents the weight corresponding to the data scale conversion rate in data relevance, and 0≤H i ≤1; The calculation formula of the scarcity is: Scarcity = I1*market competition level, Where I1 represents the weight corresponding to the market competition degree in data scarcity, and 0≤I i ≤1; The calculation formula of the scene adaptability is: Scenario adaptability = IF THEN ELSE (profitability >= profitability of peers, 1, profitability / profitability of peers), Among them, the profitability is WITH LOOKUP(Time), and the profitability of the peer group is WITH LOOKUP(Time); The calculation formula of the substitutability is: Substitutability = J1*market competition level + J2 / technical level, Among them, J1 represents the weight corresponding to the degree of market competition in data substitutability, and J2 represents the weight corresponding to the technical level in data substitutability. And 0≤J i ≤1.

8. A data asset value assessment system based on system dynamics according to claim 3, characterized in that: The economic subsystem to which the risk factors belong includes five auxiliary variables: value-added development risk, data security risk, management risk, circulation risk, and risk factors, and three constants: manager quality, financial risk level, and technical level. The risk factor calculation formula is: Risk factors = K1 value-added development risk* + K2* data security risk + K3* management risk + K4* circulation risk, Among them, K1 represents the weight corresponding to the circulation risk in the risk factors; K2 represents the weight corresponding to the data security risk in the risk factors; K3 represents the weight corresponding to the management risk in the risk factors; K4 represents the weight corresponding to the value-added development risk in the risk factors. And 0≤K i ≤1; The calculation formula for the value-added development risk is: Value-added development risk = L1 / technology level, Where L1 represents the weight corresponding to the technical level in the value-added development risk, and 0≤L i ≤1; The calculation formula for the data security risk is: Data security risk = M1 / technical level, Wherein, M1 represents the weight corresponding to the technical level in the data security risk, and 0≤M i ≤1; The calculation formula for the management risk is: Management risk = N1* manager quality + N2* financial risk level, Among them, N1 represents the weight corresponding to the manager's quality in the management risk; N2 represents the weight corresponding to the financial risk level in the management risk. And 0≤N i ≤1; The calculation formula of the circulation risk is: Circulation risk = O1 data security risk* + O2* management risk + O3* value-added development risk, Among them, O1 represents the weight corresponding to the data security risk in the circulation risk; O2 represents the weight corresponding to the management risk in the circulation risk; O3 represents the weight corresponding to the value-added development risk in the circulation risk. And 0≤O i ≤1.

9. A data asset value assessment method based on system dynamics, characterized in that: The method comprises the following steps: Obtain relevant data about data assets; Calculate the cost of data assets; Calculate data attribute adjustment coefficients; Calculate the economic adjustment factor; Calculate risk factor adjustment coefficients; The data asset value is evaluated based on the data asset cost, the data attribute adjustment coefficient, the economic factor adjustment coefficient and the risk factor adjustment coefficient.

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