CN113919602B - Data value bidirectional evaluation method and system for big data transaction - Google Patents

Data value bidirectional evaluation method and system for big data transaction Download PDF

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CN113919602B
CN113919602B CN202111513342.XA CN202111513342A CN113919602B CN 113919602 B CN113919602 B CN 113919602B CN 202111513342 A CN202111513342 A CN 202111513342A CN 113919602 B CN113919602 B CN 113919602B
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郑凯
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Abstract

The invention provides a data value bidirectional evaluation method and a data value bidirectional evaluation system for big data transaction, wherein the method comprises the following steps: obtaining first data commodity information; obtaining a first data commodity characteristic; constructing a first user demand analysis model and a first data quality evaluation model; obtaining first requirement matching information and first quality evaluation information; and inputting the first requirement matching information and the first quality evaluation information as variable information into a first objective function for calculation to obtain a first response result, generating two-way value evaluation information according to the first response result, and sending the first value evaluation information and the second value evaluation information to the first data purchasing party. The method solves the technical problems that in the prior art, the matching degree of the transaction commodity and the transaction requirement is low, the transaction data pricing scientificity is insufficient, the accurate evaluation of the value of the transaction commodity cannot be realized, and the data value cannot be fully released.

Description

Data value bidirectional evaluation method and system for big data transaction
Technical Field
The invention relates to the technical field of data transaction, in particular to a data value bidirectional evaluation method and system for big data transaction.
Background
With the maturity and development of big data technology, big data is more and more widely applied to business, and examples related to interaction, integration, exchange and transaction of big data are increasing. The big data exchange is carried out accordingly. The big data exchange operation range comprises big data asset transaction, design of big data financial derivative data and related services; developing technologies such as big data cleaning and modeling; financial lever data design and service related to big data; other services approved by supervisory authorities and related departments associated with big data transactions. The big data exchange develops data futures, data financing, data mortgage and other services for data merchants, establishes a credit evaluation system of data of both parties of the transaction, increases the flow of data transaction, and accelerates the circulation speed of data. The data varieties comprise twelve types of big data of government, medical, finance, enterprise, e-commerce, energy, traffic, commodity, consumption, education, social and society.
However, the prior art has at least the following technical problems: the method has the problems that the matching degree of the transaction commodity and the transaction requirement is low, the pricing scientificity of the transaction data is insufficient, the accurate evaluation of the value of the transaction commodity cannot be realized, and the data value cannot be fully released.
Disclosure of Invention
The application provides a data value bidirectional evaluation method and system for big data transaction, and solves the technical problems that in the prior art, the matching degree of transaction commodities and transaction requirements is low, the price of transaction data is not scientific, the accurate evaluation of the value of the transaction commodities cannot be realized, and the data value cannot be fully released. The method and the device have the advantages that the accuracy and pertinence of data value evaluation of big data transaction are improved, the positioning attaching degree and the requirement matching degree of commodities are improved, an idea is provided for developing fair and fair data transaction, the adaptation degree of transaction data and requirements is improved, the data value is evaluated in two directions, and the technical effect of fully releasing the data value is achieved.
In view of the above problems, the present application provides a method and a system for bi-directionally evaluating data value of big data transaction.
In a first aspect, the present application provides a data value bidirectional evaluation method for big data transaction, wherein the method includes: acquiring first data commodity information of a transaction between a first data supplier and a first data buyer; inputting the first data commodity information into a feature extraction unit to obtain first data commodity features; constructing a first user demand analysis model and a first data quality evaluation model; inputting the first data commodity characteristics into the first user demand analysis model and the first data quality evaluation model respectively to obtain first demand matching information and first quality evaluation information; inputting the first requirement matching information and the first quality evaluation information as variable information into a first objective function for calculation to obtain a first response result, wherein the first objective function is a value evaluation function; generating two-way value evaluation information according to the first response result, wherein the two-way value evaluation information comprises first value evaluation information and second value evaluation information; and sending the first value evaluation information and the second value evaluation information to the first data purchasing party.
In another aspect, the present application provides a system for bi-directionally evaluating data value of big data transaction, wherein the system includes: the first acquisition unit is used for acquiring first data commodity information of a transaction between a first data supplier and a first data buyer; the second obtaining unit is used for inputting the first data commodity information into the feature extraction unit to obtain first data commodity features; the first construction unit is used for constructing a first user demand analysis model and a first data quality evaluation model; a third obtaining unit, configured to input the first data commodity feature into the first user demand analysis model and the first data quality assessment model respectively, and obtain first demand matching information and first quality assessment information; a fourth obtaining unit, configured to obtain a first response result by inputting the first requirement matching information and the first quality assessment information as variable information into a first objective function for calculation, where the first objective function is a value assessment function; a first generating unit configured to generate bidirectional value evaluation information according to the first response result, wherein the bidirectional value evaluation information includes first value evaluation information and second value evaluation information; a first execution unit to send the first value assessment information and the second value assessment information to the first data purchaser.
In a third aspect, the present application provides a data value bidirectional evaluation system for big data transaction, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method of any one of the first aspect when executing the program.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
the method comprises the steps that first data commodity information for transaction between a first data supplier and a first data buyer is obtained; inputting the first data commodity information into a feature extraction unit to obtain first data commodity features; constructing a first user demand analysis model and a first data quality evaluation model; inputting the first data commodity characteristics into the first user demand analysis model and the first data quality evaluation model respectively to obtain first demand matching information and first quality evaluation information; inputting the first requirement matching information and the first quality evaluation information as variable information into a first objective function for calculation to obtain a first response result, wherein the first objective function is a value evaluation function; generating two-way value evaluation information according to the first response result, wherein the two-way value evaluation information comprises first value evaluation information and second value evaluation information; the technical scheme is that the first price evaluation information and the second price evaluation information are sent to the first data purchasing party, and the data value bidirectional evaluation method and the data value bidirectional evaluation system for big data transaction are provided, so that the accuracy and pertinence of data value evaluation of big data transaction are improved, the positioning attaching degree and the requirement matching degree of commodities are improved, an idea is provided for developing fair and fair data transaction, the adaptation degree of transaction data and requirements is improved, the data value bidirectional evaluation is realized, and the technical effect of fully releasing the data value is achieved.
The foregoing description is only an overview of the technical solutions of the present application, and the present application can be implemented according to the content of the description in order to make the technical means of the present application more clearly understood, and the following detailed description of the present application is given in order to make the above and other objects, features, and advantages of the present application more clearly understandable.
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FIG. 1 is a schematic flow chart illustrating a method for bi-directionally evaluating data value of big data transaction according to an embodiment of the present application;
FIG. 2 is a schematic flow chart illustrating a first evaluation dimension obtained by a bidirectional data value evaluation method for big data transaction according to an embodiment of the present disclosure;
FIG. 3 is a schematic flow chart illustrating a second evaluation dimension obtained by the bidirectional data value evaluation method for big data transaction according to the embodiment of the present application;
FIG. 4 is a schematic flow chart illustrating a first connection relationship construction method for bidirectional data value evaluation of big data transaction according to an embodiment of the present disclosure;
FIG. 5 is a schematic flow chart illustrating a first response result obtained by the bidirectional data value evaluation method for big data transaction according to the embodiment of the present application;
FIG. 6 is a schematic flow chart illustrating a first user demand analysis model constructed by a bidirectional data value evaluation method for big data transaction according to an embodiment of the present disclosure;
FIG. 7 is a schematic structural diagram of a data value bidirectional evaluation system for big data transaction according to an embodiment of the present application;
fig. 8 is a schematic structural diagram of an exemplary electronic device according to an embodiment of the present application.
Description of reference numerals: the device comprises a first obtaining unit 11, a second obtaining unit 12, a first constructing unit 13, a third obtaining unit 14, a fourth obtaining unit 15, a first generating unit 16, a first executing unit 17, an electronic device 300, a memory 301, a processor 302, a communication interface 303 and a bus architecture 304.
Detailed Description
The application provides a data value bidirectional evaluation method and system for big data transaction, and solves the technical problems that in the prior art, the matching degree of transaction commodities and transaction requirements is low, the price of transaction data is not scientific, the accurate evaluation of the value of the transaction commodities cannot be realized, and the data value cannot be fully released. The method and the device have the advantages that the accuracy and pertinence of data value evaluation of big data transaction are improved, the positioning attaching degree and the requirement matching degree of commodities are improved, an idea is provided for developing fair and fair data transaction, the adaptation degree of transaction data and requirements is improved, the data value is evaluated in two directions, and the technical effect of fully releasing the data value is achieved.
Summary of the application
With the maturity and development of big data technology, big data is more and more widely applied to business, and examples related to interaction, integration, exchange and transaction of big data are increasing. The big data exchange is carried out accordingly. The big data exchange operation range comprises big data asset transaction, design of big data financial derivative data and related services; developing technologies such as big data cleaning and modeling; financial lever data design and service related to big data; other services approved by supervisory authorities and related departments associated with big data transactions. The big data exchange develops data futures, data financing, data mortgage and other services for data merchants, establishes a credit evaluation system of data of both parties of the transaction, increases the flow of data transaction, and accelerates the circulation speed of data. The data varieties comprise twelve types of big data of government, medical, finance, enterprise, e-commerce, energy, traffic, commodity, consumption, education, social and society. The technical problems that the matching degree of the transaction commodity and the transaction requirement is low, the transaction data pricing scientificity is insufficient, the accurate evaluation of the value of the transaction commodity cannot be realized, and the data value cannot be fully released exist in the prior art.
In view of the above technical problems, the technical solution provided by the present application has the following general idea:
the application provides a data value bidirectional evaluation method for big data transaction, wherein the method comprises the following steps: acquiring first data commodity information of a transaction between a first data supplier and a first data buyer; inputting the first data commodity information into a feature extraction unit to obtain first data commodity features; constructing a first user demand analysis model and a first data quality evaluation model; inputting the first data commodity characteristics into the first user demand analysis model and the first data quality evaluation model respectively to obtain first demand matching information and first quality evaluation information; inputting the first requirement matching information and the first quality evaluation information as variable information into a first objective function for calculation to obtain a first response result, wherein the first objective function is a value evaluation function; generating two-way value evaluation information according to the first response result, wherein the two-way value evaluation information comprises first value evaluation information and second value evaluation information; and sending the first value evaluation information and the second value evaluation information to the first data purchasing party.
Having thus described the general principles of the present application, various non-limiting embodiments thereof will now be described in detail with reference to the accompanying drawings.
Example one
As shown in fig. 1, an embodiment of the present application provides a data value bidirectional evaluation method for big data transaction, where the method includes:
s100: acquiring first data commodity information of a transaction between a first data supplier and a first data buyer;
s200: inputting the first data commodity information into a feature extraction unit to obtain first data commodity features;
specifically, the first data supplier is a data supplier, the first data buyer is a data buyer, the commodity traded by both parties is a data analysis result rather than the data itself, the data analysis result transaction is a key point, the underlying data, namely the first data commodity information, is not directly traded through the result of data cleaning, analysis, modeling and visualization, the first data commodity information is input into the feature extraction unit for data commodity feature extraction, and thus first data commodity features such as data type, data quality, data source, data quantity and the like are obtained. Through feature extraction, the features of the data commodities can be obtained, and therefore the transaction efficiency and the transaction quality of big data transaction are improved.
S300: constructing a first user demand analysis model and a first data quality evaluation model;
s400: inputting the first data commodity characteristics into the first user demand analysis model and the first data quality evaluation model respectively to obtain first demand matching information and first quality evaluation information;
specifically, the first user demand analysis model and the first data quality evaluation model are established, the first user demand analysis model can carry out deep mining and analysis on user demands, the first data quality evaluation model can carry out scientific and accurate quality evaluation on the quality of transaction data, and a buyer can evaluate demand matching and data quality through the first user demand analysis model and the first data quality evaluation model. And further respectively inputting the first data commodity characteristics into the first user demand analysis model and the first data quality evaluation model to obtain the first demand matching information and the first quality evaluation information, and obtaining the positioning attaching degree and the demand degree of the commodity and the purchasing party based on the output information, and the data can be deeply analyzed, such as the detachability and the optimization capability.
S500: inputting the first requirement matching information and the first quality evaluation information as variable information into a first objective function for calculation to obtain a first response result, wherein the first objective function is a value evaluation function;
further, the step S500 of inputting the first requirement matching information and the first quality assessment information as variable information into a first objective function for calculation to obtain a first response result, and further includes:
s510: the calculation formula of the first objective function is as follows:
Figure DEST_PATH_IMAGE001
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE002
quality value assessment in m assessment dimensions;
Figure DEST_PATH_IMAGE003
evaluating the demand value in n evaluation dimensions;
Figure DEST_PATH_IMAGE004
solving the intersection of the quality value evaluation and the demand value evaluation;
Figure DEST_PATH_IMAGE005
weights for quality value and demand value, respectively;
Figure DEST_PATH_IMAGE006
is a predetermined threshold value.
Specifically, the first user demand analysis model is used to obtain the first demand matching information,
Figure DEST_PATH_IMAGE007
is a predetermined evaluation data, is a critical value, and represents when the evaluation data of the demand value is greater than or equal to the predetermined data
Figure 158199DEST_PATH_IMAGE007
Then, intersection set calculation of quality value evaluation and demand value evaluation is carried out, evaluation data after intersection set calculation is used as an output response result, and when the demand value evaluation is smaller than preset data
Figure 668815DEST_PATH_IMAGE007
In the process, the weight distribution calculation is carried out on the quality value evaluation and the demand value evaluation, the weight calculation result is used as the output response result,
Figure 182973DEST_PATH_IMAGE005
respectively, the weight of the quality value and the demand value. The first requirement matching information is used for carrying out requirement matching evaluation under n dimensions, for example,
Figure DEST_PATH_IMAGE008
to evaluate data for quality value in a first evaluation dimension,
Figure DEST_PATH_IMAGE009
and evaluating data for the quality value under the second evaluation dimension, and so on, wherein the evaluation dimension can be newly added and realized. The first quality evaluation information is obtained through the first data quality evaluation model, quality evaluation is conducted on the first quality evaluation information under m dimensions, the first requirement matching information and the first quality evaluation information are input into a first objective function as variable information, the first objective function is calculated through a formula, the first response result is obtained, the value of the transaction data is calculated through a value evaluation function, the transaction data value aiming at the transaction requirement can be obtained, and the data value evaluation accuracy and pertinence of the big data transaction are improved.
S600: generating two-way value evaluation information according to the first response result, wherein the two-way value evaluation information comprises first value evaluation information and second value evaluation information;
s700: and sending the first value evaluation information and the second value evaluation information to the first data purchasing party.
Specifically, two-way value evaluation information is generated according to the first response result, and the two-way value evaluation information is a comprehensive evaluation result of the transaction data value and the user requirement. Including the first price evaluation information and the second price evaluation information, since the price evaluation function is a piece function, when
Figure DEST_PATH_IMAGE010
Then, the first value evaluation information is obtained when
Figure DEST_PATH_IMAGE011
Then, the second value evaluation information is obtained. And sending the first price evaluation information and the second price evaluation information to the first data purchasing party, and also sending the first price evaluation information and the second price evaluation information to the first data supplying party to improve the data pricing and data selling capability of the data supplying party. The method can provide an idea for fair and fair data transaction of both parties of the transaction, so that the purchasing party can master the cost performance of the transaction and the adaptation degree of transaction data and requirements, thereby realizing the two-way evaluation of data value, achieving the technical effects of data opening and fully releasing the data value.
Further, as shown in fig. 2, the embodiment of the present application further includes:
s710: obtaining a first data category by performing category analysis on the first data commodity;
s720: according to the first data category, obtaining a first affiliated mathematical model of the first data purchasing party, wherein the first affiliated mathematical model is obtained through data resources to which the first data purchasing party belongs;
s730: performing model-optimizable coefficient prediction on the first affiliated mathematical model according to the first data commodity information to obtain first prediction optimization information;
s740: and taking the first prediction optimization information as a first evaluation dimension for constructing the first data quality evaluation model.
Specifically, the category of the first data commodity is analyzed to obtain a first data category, the first data category may be commodity retail data, life service data, enterprise portrait data, wood data, mobile location data, environmental data, and the like, the first data purchasing party owns the first affiliated mathematical model, the first affiliated mathematical model of the first data purchasing party is obtained by training data owned by the first data purchasing party and the first data category, and model optimization coefficient prediction is performed on the first affiliated mathematical model according to the first data commodity information. In other words, a determination is made as to whether the first data commodity can optimize an existing mathematical model of the first data purchaser. And judging the importance degree of the first data commodity to the first data purchasing party so as to obtain first prediction optimization information, and using the first prediction optimization information as a first evaluation dimension for constructing the first data quality evaluation model. Namely, the importance degree and the demand degree of the data commodity to the data purchasing party are used as a first evaluation dimension of the first data quality evaluation model. The applicability and pertinence of the first data quality evaluation model can be improved.
Further, as shown in fig. 3, the embodiment of the present application further includes:
s750: obtaining a first commodity generalization index according to the first data commodity information;
s760: obtaining a first commodity depth index according to the first data commodity information;
s770: performing data-optimizable coefficient prediction on the first data commodity information according to the first commodity generalization index and the first commodity depth index to obtain second prediction optimization information;
s780: and taking the second prediction optimization information as a second evaluation dimension for constructing the first data quality evaluation model.
Specifically, the first commodity generalization index is used for evaluating the specificity of the first data commodity, if the first commodity generalization index is too high, it indicates that the commodity is more common, and the first commodity depth index is used for evaluating the data mining depth of the first commodity, and the deeper the data mining depth, the stronger the availability of the data. And performing data optimization coefficient prediction on the first data commodity information according to the first commodity generalization index and the first commodity depth index to obtain second prediction optimization information, namely performing depth prediction on the value of the commodity data from two angles of the first commodity generalization index and the first commodity depth index. The second predictive optimization information is further used as a second evaluation dimension for constructing the first data quality evaluation model. The evaluation accuracy and the evaluation depth of the first data quality evaluation model can be improved.
Further, as shown in fig. 4, the embodiment of the present application further includes:
s810: the first user demand analysis model and the first data quality assessment model comprise a first connection relationship;
s820: the first connection relation is used for connecting an output unit of the first user demand analysis model with an input unit of the first data quality evaluation model, and the output unit of the first data quality evaluation model is connected with the input unit of the first user demand analysis model to form a closed-loop connection state;
s830: and dynamically updating the first user demand analysis model and the first data quality evaluation model by constructing the first connection relation.
Specifically, the output unit of the first user demand analysis model is connected with the input unit of the first data quality assessment model through the first connection relationship, and the output unit of the first data quality assessment model is connected with the input unit of the first user demand analysis model. In detail, the data information of the first user demand analysis model is input as part of the first data quality evaluation model to realize quality evaluation, and the output information of the first data quality evaluation model can also be input as part of the first user demand analysis model to form a closed-loop connection, and then the data output by the first user demand analysis model and the data output by the first quality evaluation model are analyzed. And repeatedly evaluating and updating the first user requirement and the first data quality, so that the conditions that a user requirement result and quality evaluation are mutually constrained, the data quality is in direct proportion to the user requirement and the like occur, the first user requirement analysis model and the first data quality evaluation model are dynamically updated by constructing the first connection relation, closed-loop connection between the models can be achieved, and dynamic update of the models is realized.
Further, as shown in fig. 5, the embodiment of the present application further includes:
s520: constructing a preset demand matching index;
s530: judging whether the first requirement matching information is larger than the preset requirement matching index or not;
s540: if the first requirement matching information is larger than or equal to the preset requirement matching index, obtaining a first calculation instruction;
s550: if the first requirement matching information is smaller than the preset requirement matching index, obtaining a second calculation instruction;
s560: and obtaining the first response result according to the first calculation instruction and the second calculation instruction.
Specifically, a preset demand matching index is constructed, and the preset demand matching index is obtained by estimating the matching degree of the data demand of the data purchasing party and the transaction data provided by the conventional purchasing platform. And judging whether the first requirement matching information is larger than the preset requirement matching index or not, and if the first requirement matching information is larger than or equal to the preset requirement matching index, obtaining a first calculation instruction, namely performing intersection calculation on quality value evaluation and demand value evaluation. And if the first requirement matching information is smaller than the preset requirement matching index, obtaining a second calculation instruction, performing value evaluation calculation, and providing scientific guidance for whether to continue trading according to a calculation result. And obtaining the first response result according to the first calculation instruction and the second calculation instruction. When data purchasing is carried out, scientific and reliable guidance opinions can be provided for data purchasing parties.
Further, as shown in fig. 6, the embodiment of the present application further includes:
s840: obtaining first enterprise information of the first data purchasing party;
s850: obtaining a first data purchasing characteristic according to the first enterprise information;
s860: matching the feature contact ratio of the first data purchasing feature and the first data commodity feature to obtain a first matching coefficient;
s870: and constructing the first user demand analysis model according to the first matching coefficient.
Specifically, the data purchasing party can be any user with data requirements, such as an enterprise engaged in big data collection and big data application development. And acquiring first enterprise information of the first data purchasing party, wherein the first enterprise is any enterprise with purchasing data requirements, and the first enterprise information comprises enterprise qualification, enterprise purchasing requirements and the like. And acquiring first data purchasing characteristics including data types (commodity retail data, life service data, enterprise portrait data, wood data, mobile position data, environment data and the like), data quality, data sources and the like according to the first enterprise information. And matching the feature contact degree of the first data purchasing feature and the first data commodity feature to obtain a first matching coefficient, namely performing feature matching on the data commodity to be provided and the data purchasing requirement, wherein the higher the first matching coefficient is, the higher the feature contact degree is, the more favorable the transaction is, and further, according to the first matching coefficient, the first user requirement analysis model is constructed. The method has the advantages that the requirements of the purchasing party are deconstructed, the first user requirement analysis model is constructed from the perspective of requirement matching, and the accuracy and the reliability of model analysis can be improved.
To sum up, the data value bidirectional evaluation method and system for big data transaction provided by the embodiment of the application have the following technical effects:
1. the method comprises the steps that first data commodity information for transaction between a first data supplier and a first data buyer is obtained; inputting the first data commodity information into a feature extraction unit to obtain first data commodity features; constructing a first user demand analysis model and a first data quality evaluation model; inputting the first data commodity characteristics into the first user demand analysis model and the first data quality evaluation model respectively to obtain first demand matching information and first quality evaluation information; inputting the first requirement matching information and the first quality evaluation information as variable information into a first objective function for calculation to obtain a first response result, wherein the first objective function is a value evaluation function; generating two-way value evaluation information according to the first response result, wherein the two-way value evaluation information comprises first value evaluation information and second value evaluation information; according to the technical scheme, the first price evaluation information and the second price evaluation information are sent to the first data purchasing party, the data value evaluation accuracy and pertinence of big data transaction are improved, the positioning attaching degree and the demand matching degree of commodities are improved, ideas are provided for developing fair and fair data transaction, the adaptation degree of transaction data and demands is improved, the data value bidirectional evaluation is achieved, and the technical effect of fully releasing the data value is achieved.
2. Due to the adoption of a sectional calculation method, the value evaluation calculation is carried out according to the demand matching index, and the technical effect of providing scientific and reliable guidance opinions for a data purchasing party during data purchasing can be achieved.
Example two
Based on the same inventive concept as the data value bidirectional evaluation method of the big data transaction in the foregoing embodiment, as shown in fig. 7, an embodiment of the present application provides a data value bidirectional evaluation system of the big data transaction, where the system includes:
a first obtaining unit 11, where the first obtaining unit 11 is configured to obtain first data commodity information of a transaction between a first data supplier and a first data buyer;
a second obtaining unit 12, where the second obtaining unit 12 is configured to input the first data commodity information into a feature extraction unit, and obtain a first data commodity feature;
the first construction unit 13 is used for constructing a first user demand analysis model and a first data quality evaluation model;
a third obtaining unit 14, where the third obtaining unit 14 is configured to input the first data commodity feature into the first user demand analysis model and the first data quality evaluation model respectively, and obtain first demand matching information and first quality evaluation information;
a fourth obtaining unit 15, where the fourth obtaining unit 15 is configured to obtain a first response result by inputting the first requirement matching information and the first quality assessment information as variable information into a first objective function for calculation, where the first objective function is a value assessment function;
a first generating unit 16, wherein the first generating unit 16 is used for generating two-way value evaluation information according to the first response result, and the two-way value evaluation information comprises first value evaluation information and second value evaluation information;
a first execution unit 17, wherein the first execution unit 17 is configured to send the first price evaluation information and the second price evaluation information to the first data purchasing party.
Further, the system comprises:
a fifth obtaining unit configured to obtain a first data category by performing category analysis on the first data commodity;
a sixth obtaining unit, configured to obtain, according to the first data category, a first affiliated mathematical model of the first data purchasing party, where the first affiliated mathematical model is obtained through a data resource to which the first data purchasing party belongs;
a seventh obtaining unit, configured to perform model-optimizable coefficient prediction on the first belonged mathematical model according to the first data commodity information, to obtain first prediction optimization information;
a second execution unit to use the first predictive optimization information as a first evaluation dimension for building the first data quality evaluation model.
Further, the system comprises:
an eighth obtaining unit, configured to obtain a first commodity generalization index according to the first data commodity information;
a ninth obtaining unit, configured to obtain a first commodity depth index according to the first data commodity information;
a tenth obtaining unit, configured to perform data-optimizable coefficient prediction on the first data commodity information according to the first commodity generalization index and the first commodity depth index, and obtain second prediction optimization information;
a third execution unit to use the second predictive optimization information as a second evaluation dimension for building the first data quality evaluation model.
Further, the system comprises:
a first connection unit, wherein the first connection unit comprises a first connection relation between the first user demand analysis model and the first data quality evaluation model;
a fourth execution unit, configured to connect, in the first connection relationship, an output unit of the first user demand analysis model with an input unit of the first data quality assessment model, and connect the output unit of the first data quality assessment model with the input unit of the first user demand analysis model, so as to form a closed-loop connection state;
a fifth execution unit, configured to dynamically update the first user demand analysis model and the first data quality assessment model by constructing the first connection relationship.
Further, the system comprises:
the second construction unit is used for constructing a preset requirement matching index;
a sixth execution unit, configured to determine whether the first requirement matching information is greater than the preset requirement matching index;
an eleventh obtaining unit, configured to obtain a first calculation instruction if the first requirement matching information is greater than or equal to the preset requirement matching index;
a twelfth obtaining unit, configured to obtain a second calculation instruction if the first requirement matching information is smaller than the preset requirement matching index;
a thirteenth obtaining unit configured to obtain the first response result according to the first calculation instruction and the second calculation instruction.
Further, the system comprises:
a fourteenth obtaining unit, configured to obtain first business information of the first data purchasing party;
a fifteenth obtaining unit, configured to obtain a first data procurement characteristic according to the first enterprise information;
a sixteenth obtaining unit, configured to perform feature overlap ratio matching on the first data purchase feature and the first data commodity feature to obtain a first matching coefficient;
a third constructing unit, configured to construct the first user demand analysis model according to the first matching coefficient.
Exemplary electronic device
The electronic apparatus of the embodiment of the present application is described below with reference to fig. 8.
Based on the same inventive concept as the data value bidirectional evaluation method of the big data transaction in the foregoing embodiment, the embodiment of the present application further provides a data value bidirectional evaluation system of the big data transaction, including: a processor coupled to a memory, the memory for storing a program that, when executed by the processor, causes a system to perform the method of any of the first aspects.
The electronic device 300 includes: processor 302, communication interface 303, memory 301. Optionally, the electronic device 300 may also include a bus architecture 304. Wherein, the communication interface 303, the processor 302 and the memory 301 may be connected to each other through a bus architecture 304; the bus architecture 304 may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus architecture 304 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown in FIG. 8, but this is not intended to represent only one bus or type of bus.
Processor 302 may be a CPU, microprocessor, ASIC, or one or more integrated circuits for controlling the execution of programs in accordance with the teachings of the present application.
The communication interface 303 is a system using any transceiver or the like, and is used for communicating with other devices or communication networks, such as ethernet, Radio Access Network (RAN), Wireless Local Area Network (WLAN), wired access network, and the like.
The memory 301 may be, but is not limited to, a ROM or other type of static storage device that can store static information and instructions, a RAM or other type of dynamic storage device that can store information and instructions, an Electrically Erasable Programmable Read Only Memory (EEPROM), a compact disc read only memory (CD ROM) or other optical disk storage, optical disk storage (including compact disc, laser disc, optical disc, digital versatile disc, blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. The memory may be self-contained and coupled to the processor through a bus architecture 304. The memory may also be integral to the processor.
The memory 301 is used for storing computer-executable instructions for executing the present application, and is controlled by the processor 302 to execute. The processor 302 is used for executing computer-executable instructions stored in the memory 301, so as to implement a data value bidirectional evaluation method for big data transaction provided by the above-mentioned embodiment of the present application.
Optionally, the computer-executable instructions in the embodiments of the present application may also be referred to as application program codes, which are not specifically limited in the embodiments of the present application.
The embodiment of the application provides a data value bidirectional evaluation method for big data transaction, wherein the method comprises the following steps: acquiring first data commodity information of a transaction between a first data supplier and a first data buyer; inputting the first data commodity information into a feature extraction unit to obtain first data commodity features; constructing a first user demand analysis model and a first data quality evaluation model; inputting the first data commodity characteristics into the first user demand analysis model and the first data quality evaluation model respectively to obtain first demand matching information and first quality evaluation information; inputting the first requirement matching information and the first quality evaluation information as variable information into a first objective function for calculation to obtain a first response result, wherein the first objective function is a value evaluation function; generating two-way value evaluation information according to the first response result, wherein the two-way value evaluation information comprises first value evaluation information and second value evaluation information; and sending the first value evaluation information and the second value evaluation information to the first data purchasing party.
Those of ordinary skill in the art will understand that: the various numbers of the first, second, etc. mentioned in this application are only used for the convenience of description and are not used to limit the scope of the embodiments of this application, nor to indicate the order of precedence. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "at least one" means one or more. At least two means two or more. "at least one," "any," or similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, at least one (one ) of a, b, or c, may represent: a, b, c, a b, a c, b c, or a b c, wherein a, b, c may be single or plural.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the application to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable system. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website site, computer, server, or data center to another website site, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device including one or more available media integrated servers, data centers, and the like. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
The various illustrative logical units and circuits described in this application may be implemented or operated upon by general purpose processors, digital signal processors, Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other programmable logic systems, discrete gate or transistor logic, discrete hardware components, or any combination thereof. A general-purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing systems, e.g., a digital signal processor and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a digital signal processor core, or any other similar configuration.
The steps of a method or algorithm described in the embodiments herein may be embodied directly in hardware, in a software element executed by a processor, or in a combination of the two. The software cells may be stored in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD ROM, or any other form of storage medium known in the art. For example, a storage medium may be coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC, which may be disposed in a terminal. In the alternative, the processor and the storage medium may reside in different components within the terminal. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Although the present application has been described in conjunction with specific features and embodiments thereof, it will be evident that various modifications and combinations can be made thereto without departing from the spirit and scope of the application. Accordingly, the specification and figures are merely exemplary of the present application as defined in the appended claims and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of the present application. It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations.

Claims (8)

1. A data value two-way evaluation method for big data transaction, wherein the method comprises the following steps:
acquiring first data commodity information of a transaction between a first data supplier and a first data buyer;
inputting the first data commodity information into a feature extraction unit to obtain first data commodity features;
constructing a first user demand analysis model and a first data quality evaluation model;
inputting the first data commodity characteristics into the first user demand analysis model and the first data quality evaluation model respectively to obtain first demand matching information and first quality evaluation information;
inputting the first requirement matching information and the first quality evaluation information as variable information into a first objective function for calculation to obtain a first response result, wherein the first objective function is a value evaluation function;
generating two-way value evaluation information according to the first response result, wherein the two-way value evaluation information comprises first value evaluation information and second value evaluation information;
sending the first value evaluation information and the second value evaluation information to the first data purchasing party;
the first requirement matching information and the first quality evaluation information are input into a first objective function as variable information to be calculated, so as to obtain a first response result, wherein a calculation formula of the first objective function is as follows:
Figure 501827DEST_PATH_IMAGE001
wherein x isiQuality value assessment in m assessment dimensions; x is the number ofjEvaluating the demand value in n evaluation dimensions; p (x)i∩xj) Solving the intersection of the quality value evaluation and the demand value evaluation; alpha and beta are respectively the weight of the quality value and the demand value; a is a preset critical value.
2. The method of claim 1, wherein the method further comprises:
obtaining a first data category by performing category analysis on the first data commodity;
according to the first data category, obtaining a first affiliated mathematical model of the first data purchasing party, wherein the first affiliated mathematical model is obtained through data resources to which the first data purchasing party belongs;
performing model-optimizable coefficient prediction on the first affiliated mathematical model according to the first data commodity information to obtain first prediction optimization information;
and taking the first prediction optimization information as a first evaluation dimension for constructing the first data quality evaluation model.
3. The method of claim 2, wherein the method further comprises:
obtaining a first commodity generalization index according to the first data commodity information;
obtaining a first commodity depth index according to the first data commodity information;
performing data-optimizable coefficient prediction on the first data commodity information according to the first commodity generalization index and the first commodity depth index to obtain second prediction optimization information;
and taking the second prediction optimization information as a second evaluation dimension for constructing the first data quality evaluation model.
4. The method of claim 1, wherein the method further comprises:
the first user demand analysis model and the first data quality assessment model comprise a first connection relationship;
the first connection relation is used for connecting an output unit of the first user demand analysis model with an input unit of the first data quality evaluation model, and the output unit of the first data quality evaluation model is connected with the input unit of the first user demand analysis model to form a closed-loop connection state;
and dynamically updating the first user demand analysis model and the first data quality evaluation model by constructing the first connection relation.
5. The method of claim 4, wherein the method further comprises:
constructing a preset demand matching index;
judging whether the first requirement matching information is larger than the preset requirement matching index or not;
if the first requirement matching information is larger than or equal to the preset requirement matching index, obtaining a first calculation instruction;
if the first requirement matching information is smaller than the preset requirement matching index, obtaining a second calculation instruction;
and obtaining the first response result according to the first calculation instruction and the second calculation instruction.
6. The method of claim 1, wherein the method further comprises:
obtaining first enterprise information of the first data purchasing party;
obtaining a first data purchasing characteristic according to the first enterprise information;
matching the feature contact ratio of the first data purchasing feature and the first data commodity feature to obtain a first matching coefficient;
and constructing the first user demand analysis model according to the first matching coefficient.
7. A system for bi-directional assessment of data value of big data transactions, wherein the system comprises:
the first acquisition unit is used for acquiring first data commodity information of a transaction between a first data supplier and a first data buyer;
the second obtaining unit is used for inputting the first data commodity information into the feature extraction unit to obtain first data commodity features;
the first construction unit is used for constructing a first user demand analysis model and a first data quality evaluation model;
a third obtaining unit, configured to input the first data commodity feature into the first user demand analysis model and the first data quality assessment model respectively, and obtain first demand matching information and first quality assessment information;
a fourth obtaining unit, configured to obtain a first response result by inputting the first requirement matching information and the first quality assessment information as variable information into a first objective function for calculation, where the first objective function is a value assessment function;
a first generating unit configured to generate bidirectional value evaluation information according to the first response result, wherein the bidirectional value evaluation information includes first value evaluation information and second value evaluation information;
a first execution unit, configured to send the first price evaluation information and the second price evaluation information to the first data purchasing party;
the first requirement matching information and the first quality evaluation information are input into a first objective function as variable information to be calculated, so as to obtain a first response result, wherein a calculation formula of the first objective function is as follows:
Figure 245399DEST_PATH_IMAGE002
wherein x isiQuality value assessment in m assessment dimensions; x is the number ofjEvaluating the demand value in n evaluation dimensions; p (x)i∩xj) Solving the intersection of the quality value evaluation and the demand value evaluation; alpha and beta are respectively the weight of the quality value and the demand value; a is a preset critical value.
8. A system for bi-directional assessment of data value for big data transactions, comprising: a processor coupled to a memory, the memory for storing a program that, when executed by the processor, causes a system to perform the method of any of claims 1-6.
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