Data asset transaction system and method

Abstract

The invention provides a data asset transaction system and method. The data asset transaction system comprises a data quality evaluation module, a data demand evaluation module, a use potential evaluation module, a transaction price evaluation module and a transaction display analysis module. The data quality evaluation module is used for estimating the quality of to-be-transacted data, the data demand evaluation module is used for estimating the demand degree of the to-be-transacted data, the use potential evaluation module is used for estimating the use potential of the to-be-transacted data, and the transaction price evaluation module is used for estimating the price of the to-be-transacted data. The transaction display and analysis module displays and analyzes data asset transaction information based on the electronic map; the system can realize the transaction of data assets between enterprises and promote the development of data economy.

Description

Technical Field

[0001] The present invention relates to the field of software informatization, and in particular to a data asset trading system and method. Background Art

[0002] Data assets refer to various types of data produced by enterprises in the course of their operations. They are new types of production factors. Currently, there is an urgent need to develop a data asset trading system to achieve data sharing and circulation among different enterprises and promote the development of the data economy. Summary of the invention

[0003] In order to realize the transaction of data assets between enterprises, the present invention designs a data asset transaction system and method.

[0004] The technical solution adopted by the present invention is a data asset trading system: It includes data quality assessment module, data demand assessment module, usage potential assessment module, transaction price estimation module and transaction display analysis module.

[0005] The data quality assessment module is used to estimate the quality of the transaction data, specifically in the following ways: A1, compare the transaction data with the data from the trusted source and calculate the accuracy score of the transaction data A0=N c ÷N t , where N c is the number of accurate data; N t is the total amount of data to be traded, that is, the total number of data entries in the data set to be traded.

[0006] A2, by calculating the ratio of the number of missing values ​​in the transaction data set to the total number of data entries, the integrity score of the transaction data is obtained. m ÷N t ), where N m is the number of missing values, that is, the number of missing data entries in the dataset.

[0007] A3, by calculating the time difference between the last update time of the data and the current time, the real-time score of the transaction data is obtained, T=1-(D0÷M), where D0 is the time difference between the last update time of the data and the present time, and M is the set maximum tolerance time.

[0008] A4, combining accuracy, completeness and real-time performance to build a comprehensive data quality scoring model to obtain the data quality score Q=W1×A0+W2×C0+W3×T, where W1, W2, and W3 are the weights of accuracy, completeness, and real-time performance, respectively, and W1+W2+W3=1.

[0009] The data demand assessment module is used to estimate the demand for the data to be traded, specifically in the following ways: B1, calculate the request rate of the data to be traded V0 = V1 ÷ V2, where V1 is the request volume of the data to be traded, and V2 is the sum of the request volumes of all data in the data asset trading system.

[0010] B2, calculate the collection rate of the data to be traded Z0 = Z1 ÷ Z2, where Z1 is the collection volume of the data to be traded, and Z2 is the sum of the collection volumes of all data in the data asset trading system.

[0011] B3, the demand score of the transaction data S0 = (V0 × Z0) ÷ A e , where A e It is the total amount of all data in the data asset trading system, that is, the total number of data entries in the data asset trading system.

[0012] The usage potential evaluation module is used to estimate the usage potential of the data to be traded, specifically in the following manner: C1, the economic scale growth rate of the field where the transaction data is located G0=(G n -G n-1 )÷G n-1 , where G n G is the gross output value of the field where the transaction data is located in the previous year. n-1 It is the gross output value of the field where the transaction data is located in the previous year.

[0013] C2, the degree of competition for similar data in the data asset trading system J = 1 ÷ N f , where N f is the number of suppliers that can provide similar data.

[0014] C3, usage potential score of the transaction data P = G0 × (1 + α × S0) β ×(1+B e ) γ ×e (-δ×J) , where B e is the number of application scenarios of the transaction data; α, β, γ, δ are adjustment parameters; (1+α×S0) β Indicates the impact of the demand for the transaction data on its usage potential. As the demand increases, the usage potential increases rapidly; (1+B e ) γ It indicates the impact of the number of application scenarios of the transaction data on its usage potential. As the number of application scenarios increases, the usage potential increases rapidly. (-δ×J) The introduction of a negative exponential factor indicates that the potential use of the data to be traded decreases rapidly as the intensity of competition increases; (1+α×S0) β 、(1+B e )γ The addition of 1 in the formula provides a baseline value to ensure that even if S0, B e When the value of is very small, the usage potential will not drop to zero or negative values.

[0015] The transaction price estimation module is used to estimate the price of the data to be traded, specifically in the following way: D1, sort the quotations of similar data in the data asset trading system from low to high; When the number of quotations for the same type of data in the data asset trading system is an odd number, the base price B0 is the value of the [(N0+1)÷2]th quotation; When the number N0 of quotations for the same type of data in the data asset trading system is an even number, the base price B0 is the average of the (N0÷2)th and [(N0÷2)+1]th quotations.

[0016] D2, the price of the data to be traded is U=B0+k1×ln(1+Q)+k2×S0+k3×P, where k1, k2, and k3 are adjustment coefficients with units, and the unit is yuan / score. Through ln(1+Q), the price increases slowly as the quality increases, and an extreme increase in the quality score will not lead to a sharp increase in the price.

[0017] The transaction display and analysis module displays and analyzes data asset transaction information based on electronic maps in the following specific ways: E1, the data asset purchasing unit pays the selling unit in the system to complete the transaction.

[0018] E2, mark the positions of the selling units and buying units in the data asset transaction order on the electronic map, use a line segment with an arrow to mark the positions of the selling units and the buying units, and the direction of the arrow is toward the position of the buying unit. The color of the line segment with an arrow is filled according to the type of data traded, and different transaction data types use different colors.

[0019] E3, mark the active trading area on the electronic map, set the radius r of the active area, the transaction density threshold ρ, the number of selling units in the data asset transaction order is S1, and the number of buying units in the data asset transaction order is S2; when the selling units in the transaction order appear repeatedly, they are counted as one selling unit, and when the buying units in the transaction order appear repeatedly, they are counted as one buying unit; When S1÷(π×r 2 )≥ρ, the circular active area is filled with orange, and the "warehouse" icon flashes in the center of the area; When S2÷(π×r 2 )≥ρ, the circular active area is filled with blue, and the "shopping cart" icon flashes in the center of the area; Adjust the values ​​of r and ρ to avoid overlapping of orange and blue filled areas. Filled areas of the same color can overlap.

[0020] E4, set x The overlapping area of ​​the orange filled areas is S x ,set up y The overlapping area of ​​the blue filled areas is S y ,So x The orange filled area data asset sales activity index I x =S x ÷[ x ×(π×r 2 )], y The blue filled area data asset purchase activity index F y =S y ÷[ y ×(π×r 2 )].

[0021] E5, Calculation x The trend of data asset sales activity index changes in the orange filled area: I x (t+1)=ε×I x (t)+(1-ε)×I x (t-1); calculate y The trend of data asset sales activity index changes in the blue filled area: I y (t+1)=ε×I y (t)+(1-ε)×I y (t-1); Where ε is the smoothing coefficient, which determines the influence of past data on the forecast; I x (t+1) is the next time node x The orange filled area is the data asset sales activity index; x (t) is the current time node x The orange filled area is the data asset sales activity index; x (t-1) is the previous time node x The orange filled area is the data asset sales activity index; y (t+1) is the next time node y The data asset sales activity index of the blue filled area; y (t) is the current time node y The data asset sales activity index of the blue filled area; y (t-1) is the previous time node yThe blue filled area is the data asset sales activity index.

[0022] A data asset trading method based on a data asset trading system: Step 1: Estimate the quality of the data to be traded. The specific method is: A1, compare the transaction data with the data from the trusted source and calculate the accuracy score of the transaction data A0=N c ÷N t , where N c is the number of accurate data; N t is the total amount of data to be traded, that is, the total number of data entries in the data set to be traded.

[0023] A2, by calculating the ratio of the number of missing values ​​in the transaction data set to the total number of data entries, the integrity score of the transaction data is obtained. m ÷N t ), where N m is the number of missing values, that is, the number of missing data entries in the dataset.

[0024] A3, by calculating the time difference between the last update time of the data and the current time, the real-time score of the transaction data is obtained, T=1-(D0÷M), where D0 is the time difference between the last update time of the data and the present time, and M is the set maximum tolerance time.

[0025] A4, combining accuracy, completeness and real-time performance to build a comprehensive data quality scoring model to obtain the data quality score Q=W1×A0+W2×C0+W3×T, where W1, W2, and W3 are the weights of accuracy, completeness, and real-time performance, respectively, and W1+W2+W3=1.

[0026] Step 2: Estimate the demand for the data to be traded. The specific method is: B1, calculate the request rate of the data to be traded V0 = V1 ÷ V2, where V1 is the request volume of the data to be traded, and V2 is the sum of the request volumes of all data in the data asset trading system.

[0027] B2, calculate the collection rate of the data to be traded Z0 = Z1 ÷ Z2, where Z1 is the collection volume of the data to be traded, and Z2 is the sum of the collection volumes of all data in the data asset trading system.

[0028] B3, the demand score of the transaction data S0 = (V0 × Z0) ÷ A e , where A e It is the total amount of all data in the data asset trading system, that is, the total number of data entries in the data asset trading system.

[0029] Step 3: Estimate the usage potential of the data to be traded, specifically: C1, the economic scale growth rate of the field where the transaction data is located G0=(G n -G n-1 )÷G n-1 , where G n G is the gross output value of the field where the transaction data is located in the previous year. n-1 It is the gross output value of the field where the transaction data is located in the previous year.

[0030] C2, the degree of competition for similar data in the data asset trading system J = 1 ÷ N f , where N f is the number of suppliers that can provide similar data.

[0031] C3, usage potential score of the transaction data P = G0 × (1 + α × S0) β ×(1+B e ) γ ×e (-δ×J) , where B e is the number of application scenarios of the transaction data; α, β, γ, δ are adjustment parameters; (1+α×S0) β Indicates the impact of the demand for the transaction data on its usage potential. As the demand increases, the usage potential increases rapidly; (1+B e ) γ It indicates the impact of the number of application scenarios of the transaction data on its usage potential. As the number of application scenarios increases, the usage potential increases rapidly. (-δ×J) The introduction of a negative exponential factor indicates that the potential use of the data to be traded decreases rapidly as the intensity of competition increases; (1+α×S0) β 、(1+B e ) γ The addition of 1 in the formula provides a baseline value to ensure that even if S0, B e When the value of is very small, the usage potential will not drop to zero or negative values.

[0032] Step 4: Estimate the price of the data to be traded. The specific method is: D1, sort the quotations of similar data in the data asset trading system from low to high; When the number of quotations for the same type of data in the data asset trading system is an odd number, the base price B0 is the value of the [(N0+1)÷2]th quotation; When the number N0 of quotations for the same type of data in the data asset trading system is an even number, the base price B0 is the average of the (N0÷2)th and [(N0÷2)+1]th quotations.

[0033] D2, the price of the data to be traded is U=B0+k1×ln(1+Q)+k2×S0+k3×P, where k1, k2, and k3 are adjustment coefficients with units, and the unit is yuan / score. Through ln(1+Q), the price increases slowly as the quality increases, and an extreme increase in the quality score will not lead to a sharp increase in the price.

[0034] Step 5: Display and analyze data asset transaction information based on electronic maps. The specific method is as follows: E1, the data asset purchasing unit pays the selling unit in the system to complete the transaction.

[0035] E2, mark the positions of the selling units and buying units in the data asset transaction order on the electronic map, use a line segment with an arrow to mark the positions of the selling units and the buying units, and the direction of the arrow is toward the position of the buying unit. The color of the line segment with an arrow is filled according to the type of data traded, and different transaction data types use different colors.

[0036] E3, mark the active trading area on the electronic map, set the radius r of the active area, the transaction density threshold ρ, the number of selling units in the data asset transaction order is S1, and the number of buying units in the data asset transaction order is S2; when the selling units in the transaction order appear repeatedly, they are counted as one selling unit, and when the buying units in the transaction order appear repeatedly, they are counted as one buying unit; When S1÷(π×r 2 )≥ρ, the circular active area is filled with orange, and the "warehouse" icon flashes in the center of the area; When S2÷(π×r 2 )≥ρ, the circular active area is filled with blue, and the "shopping cart" icon flashes in the center of the area; Adjust the values ​​of r and ρ to avoid overlapping of orange and blue filled areas. Filled areas of the same color can overlap.

[0037] E4, set x The overlapping area of ​​the orange filled areas is S x ,set up y The overlapping area of ​​the blue filled areas is S y ,So x The orange filled area data asset sales activity index I x =S x ÷[ x ×(π×r 2 )], y The blue filled area data asset purchase activity index F y =S y ÷[ y ×(π×r 2 )].

[0038] E5, Calculation x The trend of data asset sales activity index changes in the orange filled area: I x (t+1)=ε×I x (t)+(1-ε)×I x (t-1); calculate y The trend of data asset sales activity index changes in the blue filled area: I y (t+1)=ε×I y (t)+(1-ε)×I y (t-1); Where ε is the smoothing coefficient, which determines the influence of past data on the forecast; I x (t+1) is the next time node x The orange filled area is the data asset sales activity index; x (t) is the current time node x The orange filled area is the data asset sales activity index; x (t-1) is the previous time node x The orange filled area is the data asset sales activity index; y (t+1) is the next time node y The data asset sales activity index of the blue filled area; y (t) is the current time node y The data asset sales activity index of the blue filled area; y (t-1) is the previous time node y The blue filled area is the data asset sales activity index.

[0039] The data asset trading system and method of the present invention have the following advantages: (1) Design calculation methods for data quality, data demand, and data usage potential based on the characteristics of data assets, and make the design reasonable, reliable, and novel; (2) Taking into account data quality, data demand, and data usage potential, design a price estimation and calculation method for the data assets to be traded, which should be significantly creative; (3) The display and analysis of transaction information based on electronic maps is ingenious.

[0040] Other features and advantages of the present invention will be set forth in the following description or may be learned by practicing the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The drawings are only for the purpose of illustrating particular embodiments and are not to be considered limiting of the present invention. Like reference symbols denote like components throughout the drawings.

[0042] Figure 1 It is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0043] The following will further describe in detail a data asset trading system and method of the present invention in conjunction with the accompanying drawings and embodiments.

[0044] The technical solution adopted by the present invention is a data asset trading system: It includes data quality assessment module, data demand assessment module, usage potential assessment module, transaction price estimation module and transaction display analysis module.

[0045] The data quality assessment module is used to estimate the quality of the transaction data, specifically in the following ways: A1, compare the transaction data with the data from the trusted source and calculate the accuracy score of the transaction data A0=N c ÷N t , where N c is the number of accurate data; N t is the total amount of data to be traded, that is, the total number of data entries in the data set to be traded.

[0046] A2, by calculating the ratio of the number of missing values ​​in the transaction data set to the total number of data entries, the integrity score of the transaction data is obtained. m ÷N t ), where N m is the number of missing values, that is, the number of missing data entries in the dataset.

[0047] A3, by calculating the time difference between the last update time of the data and the current time, the real-time score of the transaction data is obtained, T=1-(D0÷M), where D0 is the time difference between the last update time of the data and the present time, and M is the set maximum tolerance time.

[0048] A4, combining accuracy, completeness and real-time performance to build a comprehensive data quality scoring model to obtain the data quality score Q=W1×A0+W2×C0+W3×T, where W1, W2, and W3 are the weights of accuracy, completeness, and real-time performance, respectively, and W1+W2+W3=1.

[0049] The data demand assessment module is used to estimate the demand for the data to be traded, specifically in the following ways: B1, calculate the request rate of the data to be traded V0 = V1 ÷ V2, where V1 is the request volume of the data to be traded, and V2 is the sum of the request volumes of all data in the data asset trading system.

[0050] B2, calculate the collection rate of the data to be traded Z0 = Z1 ÷ Z2, where Z1 is the collection volume of the data to be traded, and Z2 is the sum of the collection volumes of all data in the data asset trading system.

[0051] B3, the demand score of the transaction data S0 = (V0 × Z0) ÷ A e , where A e It is the total amount of all data in the data asset trading system, that is, the total number of data entries in the data asset trading system.

[0052] The usage potential evaluation module is used to estimate the usage potential of the data to be traded, specifically in the following manner: C1, the economic scale growth rate of the field where the transaction data is located G0=(G n -G n-1 )÷G n-1 , where G n G is the gross output value of the field where the transaction data is located in the previous year. n-1 It is the gross output value of the field where the transaction data is located in the previous year.

[0053] C2, the degree of competition for similar data in the data asset trading system J = 1 ÷ N f , where N f is the number of suppliers that can provide similar data.

[0054] C3, usage potential score of the transaction data P = G0 × (1 + α × S0) β ×(1+B e ) γ ×e (-δ×J) , where B e is the number of application scenarios of the transaction data; α, β, γ, δ are adjustment parameters; (1+α×S0) β Indicates the impact of the demand for the transaction data on its usage potential. As the demand increases, the usage potential increases rapidly; (1+B e ) γ It indicates the impact of the number of application scenarios of the transaction data on its usage potential. As the number of application scenarios increases, the usage potential increases rapidly. (-δ×J) The introduction of a negative exponential factor indicates that the potential use of the data to be traded decreases rapidly as the intensity of competition increases; (1+α×S0) β 、(1+B e ) γ The addition of 1 in the formula provides a baseline value to ensure that even if S0, Be When the value of is very small, the usage potential will not drop to zero or negative values.

[0055] The transaction price estimation module is used to estimate the price of the data to be traded, specifically in the following way: D1, sort the quotations of similar data in the data asset trading system from low to high; When the number of quotations for the same type of data in the data asset trading system is an odd number, the base price B0 is the value of the [(N0+1)÷2]th quotation; When the number N0 of quotations for the same type of data in the data asset trading system is an even number, the base price B0 is the average of the (N0÷2)th and [(N0÷2)+1]th quotations.

[0056] D2, the price of the data to be traded is U=B0+k1×ln(1+Q)+k2×S0+k3×P, where k1, k2, and k3 are adjustment coefficients with units, and the unit is yuan / score. Through ln(1+Q), the price increases slowly as the quality increases, and an extreme increase in the quality score will not lead to a sharp increase in the price.

[0057] The transaction display and analysis module displays and analyzes data asset transaction information based on electronic maps in the following specific ways: E1, the data asset purchasing unit pays the selling unit in the system to complete the transaction.

[0058] E2, mark the positions of the selling units and buying units in the data asset transaction order on the electronic map, use a line segment with an arrow to mark the positions of the selling units and the buying units, and the direction of the arrow is toward the position of the buying unit. The color of the line segment with an arrow is filled according to the type of data traded, and different transaction data types use different colors.

[0059] E3, mark the active trading area on the electronic map, set the radius r of the active area, the transaction density threshold ρ, the number of selling units in the data asset transaction order is S1, and the number of buying units in the data asset transaction order is S2; when the selling units in the transaction order appear repeatedly, they are counted as one selling unit, and when the buying units in the transaction order appear repeatedly, they are counted as one buying unit; When S1÷(π×r 2 )≥ρ, the circular active area is filled with orange, and the "warehouse" icon flashes in the center of the area; When S2÷(π×r 2 )≥ρ, the circular active area is filled with blue, and the "shopping cart" icon flashes in the center of the area; Adjust the values ​​of r and ρ to avoid overlapping of orange and blue filled areas. Filled areas of the same color can overlap.

[0060] E4, set x The overlapping area of ​​the orange filled areas is S x ,set up y The overlapping area of ​​the blue filled areas is S y ,So x The orange filled area data asset sales activity index I x =S x ÷[ x ×(π×r 2 )], y The blue filled area data asset purchase activity index F y =S y ÷[ y ×(π×r 2 )].

[0061] E5, Calculation x The trend of data asset sales activity index changes in the orange filled area: I x (t+1)=ε×I x (t)+(1-ε)×I x (t-1); calculate y The trend of data asset sales activity index changes in the blue filled area: I y (t+1)=ε×I y (t)+(1-ε)×I y (t-1); Where ε is the smoothing coefficient, which determines the influence of past data on the forecast; I x (t+1) is the next time node x The orange filled area is the data asset sales activity index; x (t) is the current time node x The orange filled area is the data asset sales activity index; x (t-1) is the previous time node x The orange filled area is the data asset sales activity index; y (t+1) is the next time node y The data asset sales activity index of the blue filled area; y (t) is the current time node y The data asset sales activity index of the blue filled area; y (t-1) is the previous time node y The blue filled area is the data asset sales activity index.

[0062] like Figure 1 As shown, a data asset trading method based on a data asset trading system: Step 1: Estimate the quality of the data to be traded. The specific method is: A1, compare the transaction data with the data from the trusted source and calculate the accuracy score of the transaction data A0=N c ÷N t , where N c is the number of accurate data; N t is the total amount of data to be traded, that is, the total number of data entries in the data set to be traded.

[0063] A2, by calculating the ratio of the number of missing values ​​in the transaction data set to the total number of data entries, the integrity score of the transaction data is obtained. m ÷N t ), where N m is the number of missing values, that is, the number of missing data entries in the dataset.

[0064] A3, by calculating the time difference between the last update time of the data and the current time, the real-time score of the transaction data is obtained, T=1-(D0÷M), where D0 is the time difference between the last update time of the data and the present time, and M is the set maximum tolerance time.

[0065] A4, combining accuracy, completeness and real-time performance to build a comprehensive data quality scoring model to obtain the data quality score Q=W1×A0+W2×C0+W3×T, where W1, W2, and W3 are the weights of accuracy, completeness, and real-time performance, respectively, and W1+W2+W3=1.

[0066] Step 2: Estimate the demand for the data to be traded. The specific method is: B1, calculate the request rate of the data to be traded V0 = V1 ÷ V2, where V1 is the request volume of the data to be traded, and V2 is the sum of the request volumes of all data in the data asset trading system.

[0067] B2, calculate the collection rate of the data to be traded Z0 = Z1 ÷ Z2, where Z1 is the collection volume of the data to be traded, and Z2 is the sum of the collection volumes of all data in the data asset trading system.

[0068] B3, the demand score of the transaction data S0 = (V0 × Z0) ÷ A e , where A e It is the total amount of all data in the data asset trading system, that is, the total number of data entries in the data asset trading system.

[0069] Step 3: Estimate the usage potential of the data to be traded, specifically: C1, the economic scale growth rate of the field where the transaction data is located G0=(G n -G n-1 )÷G n-1 , where G n G is the gross output value of the field where the transaction data is located in the previous year. n-1 It is the gross output value of the field where the transaction data is located in the previous year.

[0070] C2, the degree of competition for similar data in the data asset trading system J = 1 ÷ N f , where N f is the number of suppliers that can provide similar data.

[0071] C3, usage potential score of the transaction data P = G0 × (1 + α × S0) β ×(1+B e ) γ ×e (-δ×J) , where B e is the number of application scenarios of the transaction data; α, β, γ, δ are adjustment parameters; (1+α×S0) β Indicates the impact of the demand for the transaction data on its usage potential. As the demand increases, the usage potential increases rapidly; (1+B e ) γ It indicates the impact of the number of application scenarios of the transaction data on its usage potential. As the number of application scenarios increases, the usage potential increases rapidly. (-δ×J) The introduction of a negative exponential factor indicates that the potential use of the data to be traded decreases rapidly as the intensity of competition increases; (1+α×S0) β 、(1+B e ) γ The addition of 1 in the formula provides a baseline value to ensure that even if S0, B e When the value of is very small, the usage potential will not drop to zero or negative values.

[0072] Step 4: Estimate the price of the data to be traded. The specific method is: D1, sort the quotations of similar data in the data asset trading system from low to high; When the number of quotations for the same type of data in the data asset trading system is an odd number, the base price B0 is the value of the [(N0+1)÷2]th quotation; When the number N0 of quotations for the same type of data in the data asset trading system is an even number, the base price B0 is the average of the (N0÷2)th and [(N0÷2)+1]th quotations.

[0073] D2, the price of the data to be traded is U=B0+k1×ln(1+Q)+k2×S0+k3×P, where k1, k2, and k3 are adjustment coefficients with units, and the unit is yuan / score. Through ln(1+Q), the price increases slowly as the quality increases, and an extreme increase in the quality score will not lead to a sharp increase in the price.

[0074] Step 5: Display and analyze data asset transaction information based on electronic maps. The specific method is as follows: E1, the data asset purchasing unit pays the selling unit in the system to complete the transaction.

[0075] E2, mark the positions of the selling units and buying units in the data asset transaction order on the electronic map, use a line segment with an arrow to mark the positions of the selling units and the buying units, and the direction of the arrow is toward the position of the buying unit. The color of the line segment with an arrow is filled according to the type of data traded, and different transaction data types use different colors.

[0076] E3, mark the active trading area on the electronic map, set the radius r of the active area, the transaction density threshold ρ, the number of selling units in the data asset transaction order is S1, and the number of buying units in the data asset transaction order is S2; when the selling units in the transaction order appear repeatedly, they are counted as one selling unit, and when the buying units in the transaction order appear repeatedly, they are counted as one buying unit; When S1÷(π×r 2 )≥ρ, the circular active area is filled with orange, and the "warehouse" icon flashes in the center of the area; When S2÷(π×r 2 )≥ρ, the circular active area is filled with blue, and the "shopping cart" icon flashes in the center of the area; Adjust the values ​​of r and ρ to avoid overlapping of orange and blue filled areas. Filled areas of the same color can overlap.

[0077] E4, set x The overlapping area of ​​the orange filled areas is S x ,set up y The overlapping area of ​​the blue filled areas is S y ,So x The orange filled area data asset sales activity index I x =S x ÷[ x ×(π×r 2 )], y The blue filled area data asset purchase activity index F y =S y ÷[ y ×(π×r 2 )].

[0078] E5, Calculation x The trend of data asset sales activity index changes in the orange filled area: I x (t+1)=ε×I x (t)+(1-ε)×I x (t-1); calculate y The trend of data asset sales activity index changes in the blue filled area: I y (t+1)=ε×I y (t)+(1-ε)×I y (t-1); Where ε is the smoothing coefficient, which determines the influence of past data on the forecast; I x (t+1) is the next time node x The orange filled area is the data asset sales activity index; x (t) is the current time node x The orange filled area is the data asset sales activity index; x (t-1) is the previous time node x The orange filled area is the data asset sales activity index; y (t+1) is the next time node y The data asset sales activity index of the blue filled area; y (t) is the current time node y The data asset sales activity index of the blue filled area; y (t-1) is the previous time node y The blue filled area is the data asset sales activity index.

[0079] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. A data asset trading system, characterized by: It includes data quality assessment module, data demand assessment module, usage potential assessment module, transaction price estimation module, and transaction display analysis module; The data quality assessment module is used to estimate the quality of the transaction data, specifically in the following ways: A1, compare the transaction data with the data from the trusted source and calculate the accuracy score of the transaction data A0=N c ÷N t , where N c is the number of accurate data; N t is the total amount of data to be traded, that is, the total number of data entries in the data set to be traded; A2, by calculating the ratio of the number of missing values ​​in the transaction data set to the total number of data entries, the integrity score of the transaction data is obtained. m ÷N t ), where N m is the number of missing values, that is, the number of missing data entries in the data set; A3, by calculating the time difference between the last update time of the data and the current time, obtain the real-time score of the transaction data T=1-(D0÷M), where D0 is the time difference between the last update time of the data and the present time, and M is the set maximum tolerance time; A4, combining accuracy, completeness and real-time to build a comprehensive data quality scoring model to obtain the data quality score Q = W1 × A0 + W2 × C0 + W3 × T, where W1, W2, and W3 are the weights of accuracy, completeness, and real-time, respectively, and W1 + W2 + W3 = 1; The data demand assessment module is used to estimate the demand for the data to be traded, specifically in the following ways: B1, calculate the request rate of the data to be traded V0 = V1 ÷ V2, where V1 is the request volume of the data to be traded, and V2 is the sum of the request volumes of all data in the data asset trading system; B2, calculate the collection rate of the data to be traded Z0 = Z1 ÷ Z2, where Z1 is the collection volume of the data to be traded, and Z2 is the sum of the collection volumes of all data in the data asset trading system; B3, the demand score of the transaction data S0 = (V0 × Z0) ÷ A e , where A e The total amount of all data in the data asset trading system, that is, the total number of data entries in the data asset trading system; The usage potential evaluation module is used to estimate the usage potential of the data to be traded, specifically in the following manner: C1, the economic scale growth rate of the field where the transaction data is located G0=(G n -G n-1 )÷G n-1 , where G n G is the gross output value of the field where the transaction data is located in the previous year. n-1 The gross output value of the field where the transaction data is located in the previous year; C2, the degree of competition for similar data in the data asset trading system J = 1 ÷ N f , where N f The number of suppliers that can provide similar data; C3, usage potential score of the transaction data P = G0 × (1 + α × S0) β ×(1+B e ) γ ×e (-δ×J) , where B e is the number of application scenarios of the transaction data; α, β, γ, δ are adjustment parameters; (1+α×S0) β Indicates the impact of the demand for the transaction data on its usage potential. As the demand increases, the usage potential increases rapidly; (1+B e ) γ It indicates the impact of the number of application scenarios of the transaction data on its usage potential. As the number of application scenarios increases, the usage potential increases rapidly. (-δ×J) The introduction of a negative exponential factor indicates that the potential use of the data to be traded decreases rapidly as the intensity of competition increases; (1+α×S0) β 、(1+B e ) γ The addition of 1 in the formula provides a baseline value to ensure that even if S0, B e When the value of is very small, the usage potential will not drop to zero or negative value; The transaction price estimation module is used to estimate the price of the data to be traded, specifically in the following way: D1, sort the quotations of similar data in the data asset trading system from low to high; When the number of quotations for the same type of data in the data asset trading system is an odd number, the base price B0 is the value of the [(N0+1)÷2]th quotation; When the number of quotations for the same type of data in the data asset trading system is an even number, the benchmark price B0 is the average of the (N0÷2)th and [(N0÷2)+1]th quotations; D2, the price of the data to be traded is U=B0+k1×ln(1+Q)+k2×S0+k3×P, where k1, k2, and k3 are adjustment coefficients with units, and the unit is yuan / score. Through ln(1+Q), the price increases slowly as the quality increases, and the extreme improvement of the quality score will not lead to a sharp increase in the price. The transaction display and analysis module displays and analyzes data asset transaction information based on electronic maps in the following specific ways: E1, the data asset purchasing unit pays the selling unit in the system to complete the transaction; E2, mark the positions of the selling units and the buying units in the data asset transaction order on the electronic map, and use a line segment with an arrow to mark the positions of the selling units and the buying units, with the arrow pointing toward the buying unit. The color of the line segment with an arrow is filled according to the type of the traded data, and different colors are used for different types of transaction data; E3, mark the active trading area on the electronic map, set the radius r of the active area, the transaction density threshold ρ, the number of selling units in the data asset transaction order is S1, and the number of buying units in the data asset transaction order is S2; when the selling units in the transaction order appear repeatedly, they are counted as one selling unit, and when the buying units in the transaction order appear repeatedly, they are counted as one buying unit; When S1÷(π×r 2 )≥ρ, the circular active area is filled with orange, and the "warehouse" icon flashes in the center of the area; When S2÷(π×r 2 )≥ρ, the circular active area is filled with blue, and the "shopping cart" icon flashes in the center of the area; Adjust the values ​​of r and ρ to avoid overlapping of orange and blue filled areas. Filled areas of the same color can overlap. E4, set x The overlapping area of ​​the orange filled areas is S x ,set up y The overlapping area of ​​the blue filled areas is S y ,So x The orange filled area data asset sales activity index I x =S x ÷[ x ×(π×r 2 )], y The blue filled area data asset purchase activity index F y =S y ÷[ y ×(π×r 2 )]; E5, Calculation x The trend of data asset sales activity index changes in the orange filled area: I x (t+1)=ε×I x (t)+(1-ε)×I x (t-1); calculate y The trend of data asset sales activity index changes in the blue filled area: I y (t+1)=ε×I y (t)+(1-ε)×I y (t-1); Where ε is the smoothing coefficient, which determines the influence of past data on the forecast; I x (t+1) is the next time node x The orange filled area is the data asset sales activity index; x (t) is the current time node x The orange filled area is the data asset sales activity index; x (t-1) is the previous time node x The orange filled area is the data asset sales activity index; y (t+1) is the next time node y The data asset sales activity index of the blue filled area; y (t) is the current time node y The data asset sales activity index of the blue filled area; y (t-1) is the previous time node y The blue filled area is the data asset sales activity index.

2. The data asset trading method of the data asset trading system according to claim 1, characterized in that: Step 1: Estimate the quality of the data to be traded. The specific method is: A1, compare the transaction data with the data from the trusted source and calculate the accuracy score of the transaction data A0=N c ÷N t , where N c is the number of accurate data; N t is the total amount of data to be traded, that is, the total number of data entries in the data set to be traded; A2, by calculating the ratio of the number of missing values ​​in the transaction data set to the total number of data entries, the integrity score of the transaction data is obtained. m ÷N t ), where N m is the number of missing values, that is, the number of missing data entries in the data set; A3, by calculating the time difference between the last update time of the data and the current time, obtain the real-time score of the transaction data T=1-(D0÷M), where D0 is the time difference between the last update time of the data and the present time, and M is the set maximum tolerance time; A4, combining accuracy, completeness and real-time to build a comprehensive data quality scoring model to obtain the data quality score Q = W1 × A0 + W2 × C0 + W3 × T, where W1, W2, and W3 are the weights of accuracy, completeness, and real-time, respectively, and W1 + W2 + W3 = 1; Step 2: Estimate the demand for the data to be traded. The specific method is: B1, calculate the request rate of the data to be traded V0 = V1 ÷ V2, where V1 is the request volume of the data to be traded, and V2 is the sum of the request volumes of all data in the data asset trading system; B2, calculate the collection rate of the data to be traded Z0 = Z1 ÷ Z2, where Z1 is the collection volume of the data to be traded, and Z2 is the sum of the collection volumes of all data in the data asset trading system; B3, the demand score of the transaction data S0 = (V0 × Z0) ÷ A e , where A e The total amount of all data in the data asset trading system, that is, the total number of data entries in the data asset trading system; Step 3: Estimate the usage potential of the data to be traded, specifically: C1, the economic scale growth rate of the field where the transaction data is located G0=(G n -G n-1 )÷G n-1 , where G n G is the gross output value of the field where the transaction data is located in the previous year. n-1 The gross output value of the field where the transaction data is located in the previous year; C2, the degree of competition for similar data in the data asset trading system J = 1 ÷ N f , where N f The number of suppliers that can provide similar data; C3, usage potential score of the transaction data P = G0 × (1 + α × S0) β ×(1+B e ) γ ×e (-δ×J) , where B e is the number of application scenarios of the transaction data; α, β, γ, δ are adjustment parameters; (1+α×S0) β Indicates the impact of the demand for the transaction data on its usage potential. As the demand increases, the usage potential increases rapidly; (1+B e ) γ It indicates the impact of the number of application scenarios of the transaction data on its usage potential. As the number of application scenarios increases, the usage potential increases rapidly. (-δ×J) The introduction of a negative exponential factor indicates that the potential use of the data to be traded decreases rapidly as the intensity of competition increases; (1+α×S0) β 、(1+B e ) γ The addition of 1 in the formula provides a baseline value to ensure that even if S0, B e When the value of is very small, the usage potential will not drop to zero or negative value; Step 4: Estimate the price of the data to be traded. The specific method is: D1, sort the quotations of similar data in the data asset trading system from low to high; When the number of quotations for the same type of data in the data asset trading system is an odd number, the base price B0 is the value of the [(N0+1)÷2]th quotation; When the number of quotations for the same type of data in the data asset trading system is an even number, the benchmark price B0 is the average of the (N0÷2)th and [(N0÷2)+1]th quotations; D2, the price of the data to be traded is U=B0+k1×ln(1+Q)+k2×S0+k3×P, where k1, k2, and k3 are adjustment coefficients with units, and the unit is yuan / score. Through ln(1+Q), the price increases slowly as the quality increases, and the extreme improvement of the quality score will not lead to a sharp increase in the price. Step 5: Display and analyze data asset transaction information based on electronic maps. The specific method is as follows: E1, the data asset purchasing unit pays the selling unit in the system to complete the transaction; E2, mark the positions of the selling units and the buying units in the data asset transaction order on the electronic map, and use a line segment with an arrow to mark the positions of the selling units and the buying units, with the arrow pointing toward the buying unit. The color of the line segment with an arrow is filled according to the type of the traded data, and different colors are used for different types of transaction data; E3, mark the active trading area on the electronic map, set the radius r of the active area, the transaction density threshold ρ, the number of selling units in the data asset transaction order is S1, and the number of buying units in the data asset transaction order is S2; when the selling units in the transaction order appear repeatedly, they are counted as one selling unit, and when the buying units in the transaction order appear repeatedly, they are counted as one buying unit; When S1÷(π×r 2 )≥ρ, the circular active area is filled with orange, and the "warehouse" icon flashes in the center of the area; When S2÷(π×r 2 )≥ρ, the circular active area is filled with blue, and the "shopping cart" icon flashes in the center of the area; Adjust the values ​​of r and ρ to avoid overlapping of orange and blue filled areas. Filled areas of the same color can overlap. E4, set x The overlapping area of ​​the orange filled areas is S x ,set up y The overlapping area of ​​the blue filled areas is S y ,So x The orange filled area data asset sales activity index I x =S x ÷[ x ×(π×r 2 )], y The blue filled area data asset purchase activity index F y =S y ÷[ y ×(π×r 2 )]; E5, Calculation x The trend of data asset sales activity index changes in the orange filled area: I x (t+1)=ε×I x (t)+(1-ε)×I x (t-1); calculate y The trend of data asset sales activity index changes in the blue filled area: I y (t+1)=ε×I y (t)+(1-ε)×I y (t-1); Where ε is the smoothing coefficient, which determines the influence of past data on the forecast; I x (t+1) is the next time node x The orange filled area is the data asset sales activity index; x (t) is the current time node x The orange filled area is the data asset sales activity index; x (t-1) is the previous time node x The orange filled area is the data asset sales activity index; y (t+1) is the next time node y The data asset sales activity index of the blue filled area; y (t) is the current time node y The data asset sales activity index of the blue filled area; y (t-1) is the previous time node y The blue filled area is the data asset sales activity index.

Images