CN117114819A - Evaluation body-based data transaction reputation evaluation method - Google Patents
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- 230000002776 aggregation Effects 0.000 claims abstract description 6
- 238000004220 aggregation Methods 0.000 claims abstract description 6
- 230000006870 function Effects 0.000 claims description 10
- 238000013507 mapping Methods 0.000 claims description 9
- 238000004364 calculation method Methods 0.000 claims description 7
- 230000004044 response Effects 0.000 claims description 6
- 230000008859 change Effects 0.000 claims description 4
- 238000007405 data analysis Methods 0.000 claims description 3
- 238000012545 processing Methods 0.000 claims description 3
- 230000004913 activation Effects 0.000 claims description 2
- 238000013461 design Methods 0.000 claims description 2
- 230000007613 environmental effect Effects 0.000 claims description 2
- 238000010606 normalization Methods 0.000 claims description 2
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- 238000010586 diagram Methods 0.000 description 5
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0609—Buyer or seller confidence or verification
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
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- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/04—Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
Abstract
The invention discloses a data transaction reputation evaluation method based on an evaluation body, and belongs to the field of data transaction reputation evaluation. According to the data transaction reputation evaluation method based on the evaluation body, the reputation data of the transaction party is dynamically obtained by proposing the structure of the evaluation body, so that multidimensional reputation evaluation is realized; the reputation evaluation aggregation method combining objective indexes and feedback rating indexes is provided, a qualitative rating mode of a data set quality index is replaced by a quantitative rating mode, a static individual information index, transaction amount and time transaction context attribute index are introduced, and a qualitative rating of feedback rating is reserved to provide an interactive experience feedback path for a data requester. Based on the obtained reputation value, the credibility of the seller can be effectively evaluated, and the method can be widely applied to the field of data transaction.
Description
Technical Field
The invention relates to the field of data transaction reputation evaluation, in particular to a data transaction reputation evaluation method based on an evaluation body.
Background
The data transaction platform is uncertain and risky due to the openness and the virtualization, any person can issue data product information anywhere and anytime, a seller can issue false information for pursuing benefit maximization, so that the buyer is deceived by an illegal merchant, the user is distrust on the data transaction platform, and the sustainable development of data transaction is hindered. By establishing an efficient reputation evaluation mechanism, uncertainty and risk of electronic commerce transactions can be effectively reduced. The trust mechanism is researched in a large amount and a great deal of achievements are achieved based on different application backgrounds in the industry, and the trust mechanism is widely applied to the fields of P2P, electronic commerce, recommendation systems and social networks, but the related research is lacking in the field of data transaction. The credit data of the traditional credit evaluation model is derived from subjective evaluation of buyers, and the credit evaluation is inaccurate due to excessive qualitative evaluation.
Disclosure of Invention
Aiming at the problems, the invention aims to provide the evaluation body-based data transaction reputation evaluation method, which solves the problem of inaccurate reputation evaluation caused by excessive qualitative evaluation because reputation data are derived from subjective evaluation of buyers in the prior art, and can effectively evaluate the reliability of sellers in a data transaction scene.
The technical scheme adopted by the invention is that the data transaction credit evaluation method based on the evaluation body is characterized by comprising the following steps of:
step 1, when a user enters a system for the first time, firstly instantiating an evaluation body, then acquiring user attributes by using a getUAttr function, and mapping a reputation object into the initial evaluation body according to an initEE function；
Step 2, when a data request initiates a transaction application to a data owner, the evaluation body derives, an algorithm records transaction context attributes when the data request initiates the application, and the algorithm is used for calculating the change of the index involved from the beginning to the completion of the transaction and completing the activation of the evaluation body;
step 3, along with the occurrence of data transaction, the evaluator invokes a mapping function, establishes a mapping relation between the data requester and the evaluator, does not receive an evaluation request of the data requester within a certain time, and persists the mapping relation;
step 4, after the data transaction is completed, the data request party initiates a transaction evaluation request, and the evaluation body calls a data analysis method analyticldata to analyze the transaction data submitted by the data request party to obtainInvoking setTEvalAttr to obtain transaction evaluation attribute to generate final evaluation body；
Step 5, calling an update report method to realize a reputation value aggregation algorithm, and completing calculation and update of the reputation value;
and 6, according to the transaction fraud reporting and judging result, the credit punishment of the data owner is reduced through the fraud punishment factor for the fraudulent transaction.
The present invention is also characterized in that,
the step 1 is specifically as follows:
in order to reduce the time complexity of reputation evaluation after transaction, realize the quick acquisition of reputation index data, protect privacy information and transaction data of users from being revealed or tampered, design the evaluation body structure, when relevant reputation data is acquired, the evaluation body shields the original data, analyze and calculate the reputation data collected according to task demands, form the reputation data structure of the sole representative data owner, the user attribute includes the data integrity, auditing authority, data standardization, the corresponding non-transaction index factor P is:
；
wherein, intag represents the data integrity of the data owner, auth represents the auditing authority of the data owner, and Norm represents the data normalization of the data owner.
The step 2 is specifically as follows:
trust is constrained by time, domain, environmental factors and other contexts, and entities show different behavior abilities and transaction credibility in different context environments, so that transaction amount, transaction time and response time transaction context attribute indexes are designed. After the data requesting party initiates the transaction request, the evaluator monitors the transaction context attribute:
corresponding transaction amount factor：
；
Wherein,a transaction amount factor representing the ith transaction of the data owner,indicating the amount of the ith transaction,is the average sales of the data owners.
Corresponding transaction amount time factor：
；
Wherein,a transaction time factor representing the ith transaction of the data owner,indicating the current date of the day,representing the reputation evaluation date of the ith transaction obtained by the data owner, the reputation score obtained by the seller in 7 consecutive days is not attenuated by time, and is set to be 1 correspondingly.
Corresponding response time factor：
；
Wherein,a response time factor representing the ith transaction of the data owner,indicating the time at which the data owner responded to the data requestor transaction request,representing the time when the data requester initiates the transaction application, the reputation score obtained by the seller within 1 hour is not subject to time decay, and is correspondingly set to be 1.
The step 3 is specifically as follows:
the spatial complexity of reputation data retrieval of a data owner in data transaction is reduced, and the association relation between the user and an evaluation body is constructed in a session mode, so that the method is very useful in a system of a large-order user, and if the user performs reputation evaluation request in a session active state when the transaction occurs, information retrieval in a huge dataset can be avoided;
the step 4 is specifically as follows:
when a user initiates a reputation evaluation request, positioning an evaluation body through session, and processing transaction data by the evaluation body to obtain a data quality index value. Three reputation data of the description compliance, the service attitude and the logistics service submitted by a data requesting party are obtained through a getTEvalAttr method, five-level grading is adopted, a value range [0,5] can be finally converted into three-level grading, the value range is { -1,0,1}, an evaluation body is formed, and finally reputation value calculation and updating are carried out according to reputation evaluation rules:
corresponding data quality factor：
；
Wherein,representing the data owner's firstThe data quality factor of i transactions,an integrity index value representing an i-th transaction,a singleness index value representing the ith transaction,the data value representing the ith transaction is set to be 0.0009 when the value is smaller than the threshold value of 0.0009 in order to ensure the correctness of the calculation formula, the result after logarithmic operation is a value of a single digit level, and the change rate of the control reputation value is not too fast.
The data is used as commodity and has the characteristics of storage, easy replication and easy propagation, the data owner dishonest issues false data, the data requester cannot obtain fraud compensation through refund, and the fraud penalty factors need to satisfy the properties: (1) The data owner provides false data and is successfully reported by the data requester, and the data owner obtains fraud punishment; (2) The data owners can continuously issue false data, and the success of reporting by the data requesters can greatly reduce the credit score:
the reputation evaluation aggregation method comprises the following steps:
wherein,the score is represented by the score obtained at the ith transaction of the data owner at (1, 0, -1).
The step 6 is specifically as follows:
the fraud penalty factor is:
β=e -αx
wherein β represents a fraud penalty factor for adjusting the reputation value REP, a represents an adjustment parameter for the fraud penalty factor, a represents a value (0, 1), x represents a number of consecutive fraud, and the seller accepts the penalty when the ith transaction of the data owner is detected as a false transaction, represented by the number of fraud occurrences in a fixed period of time, rep=β.
Drawings
FIG. 1 is a system model diagram of a data transaction reputation evaluation method based on an evaluation volume according to the present invention;
FIG. 2 is an evaluation volume structure diagram of a data transaction reputation evaluation method based on an evaluation volume according to the present invention;
FIG. 3 is a timing diagram of reputation evaluation based on an evaluation volume.
Detailed Description
The invention will be described in detail below with reference to the drawings and the detailed description.
In order to realize reliability evaluation of users of a data transaction platform, a reputation value is set for each user, updating of the reputation value is completed through a reputation evaluation algorithm, and meanwhile, reputation score reduction punishment is realized for fraudulent users through a punishment algorithm;
the model maps the data owners into the evaluation bodies, when transaction events related to the data owners occur, the evaluation bodies execute corresponding functions, sub-evaluation bodies are derived to complete subsequent reputation data processing, the system generates a session to establish the connection between the data requesters and the sub-evaluation bodies, and fig. 1 depicts the system model composition of the evaluation body-based data transaction reputation evaluation method.
Fig. 2 depicts the basic constituent structure of the evaluation body. "+" marks common attributes and methods, and "-" marks private attributes and methods. When a data owner enters a transaction system, the model provided by the invention uniquely maps the data owner into a father evaluation body through a getUAttr function. An evaluator is an abstract, structured description generated by a reputation object, consisting of attributes and methods. The evaluation body encapsulates the individual credit data of the transaction main body data owner, conceals the privacy data and protects the individual information security; the method comprises the steps of integrating a contextual reputation index, a data commodity quality index and a reputation index fed back by a data requester when trading occurs. Applying a reputation system of an evaluation body, wherein the evaluation body is activated when a transaction is formed, an activated evaluation body is formed, interactive reputation data are acquired at three stages of before, during and after the transaction is carried out with both sides of the transaction, and reputation data are calculated and updated after the transaction is completed;
the key parameters and meanings used in the system model diagram of the evaluation-body-based data transaction reputation evaluation method are given below:
user, the trade entity in the trade process can be shop, shop owner;
UAttr: user attributes;
UinfoAttr: a set of individual information attributes, a proper subset of user attributes;
GreputAttr: a global set of reputation attributes, a proper subset of user attributes;
TEAttr: a transaction evaluation attribute set;
TBAttr: a transaction context attribute set;
DR: a proper subset of the data requesters, users;
DO: a proper subset of data owners, users;
EE: the evaluation body, the structural expression of the reputation object, by attribute, method make up;
EEAttr: evaluating the body attribute;
session: session, maintain DR and EE mapping.
The reputation evaluation system based on the evaluation body comprises two parts, namely a node and a data transaction platform, wherein the node is divided into a data owner and a data requester, and the data requester refers to an Internet enterprise, a manufacturer, a research institution and the like which have great demands on data; a data owner refers to an enterprise, individual, organization, etc. that holds ownership of data and can manage rights to the data.
When data is requested, the data request initiates a transaction request to a data owner, the data owner can audit the request after receiving the request of the requester, and then a data access tag is sent to the data requester, wherein the access tag contains information such as a data set access path and the like. The reputation evaluation module comprises a transaction experience evaluation, data quality evaluation, reputation information inquiry and reputation information updating function module. In the evaluation stage, the data requesting party evaluates according to different dimensionalities of the data set, calculates an index value according to an index calculation method, performs reputation calculation on the transaction, is used for marking the credibility of the user, and fig. 3 is a reputation evaluation timing diagram based on an evaluation body.
The data transaction reputation evaluation method based on the evaluation body comprises the following steps:
(1) The system receives a user registration request and acquires attribute information of a data owner through a GetUAttrs function;
(2) The system maps the reputation object RO into an evaluation body according to the information of the data owner；
(3) Corresponding attribute acquisition method of user transaction behavior triggering evaluation body to generate derivative evaluation body；
(4) Generating a session, and establishing a connection between a data requesting party and an evaluation body;
(5) The system receives a reputation evaluation request of a user, an evaluation body analyzes data quality through an analysisData method, and subjective reputation data of the user is obtained through a getTEvalAttr method;
(6) After data analysis, the credit data aggregation is completed by using an exeee method to form an evaluation body；
(7) Calculating and updating the reputation value according to the reputation evaluation rule;
。
Claims (7)
1. the data transaction reputation evaluation method based on the evaluation body is characterized by comprising the following steps of:
step 1, when a user enters a system for the first time, firstly instantiating an evaluation body, then acquiring user attributes by using a getUAttr function, and mapping a reputation object into a primary evaluation body ee according to an initEE function init ；
Step 2, when a data request initiates a transaction application to a data owner, the evaluation body derives, an algorithm records transaction context attributes when the data request initiates the application, and the algorithm is used for calculating the change of the index involved from the beginning to the completion of the transaction and completing the activation of the evaluation body;
step 3, along with the occurrence of data transaction, the evaluator invokes a mapping function, establishes a mapping relation between the data requester and the evaluator, does not receive an evaluation request of the data requester within a certain time, and persists the mapping relation;
step 4, after the data transaction is completed, the data request party initiates a transaction evaluation request, the evaluation body calls a data analysis method analyzdata to analyze the transaction data submitted by the data request party, a data quality index value is obtained, and setTEvalAttr is called to obtain a transaction evaluation attribute, so that a final evaluation body ee is generated end ；
Step 5, calling an update report method to realize a reputation value aggregation algorithm, and completing calculation and update of the reputation value;
and 6, according to the transaction fraud reporting and judging result, the credit punishment of the data owner is reduced through the fraud punishment factor for the fraudulent transaction.
2. The method for evaluating the reputation of data transactions based on an evaluation body according to claim 1, wherein the step 1 is specifically as follows:
in order to reduce the time complexity of reputation evaluation after transaction, realize the quick acquisition of reputation index data, protect privacy information and transaction data of users from being revealed or tampered, design the evaluation body structure, when relevant reputation data is acquired, the evaluation body shields the original data, analyze and calculate the reputation data collected according to task demands, form the reputation data structure of the sole representative data owner, the user attribute includes the data integrity, auditing authority, data standardization, the corresponding non-transaction index factor P is:
；
wherein, intag represents the data integrity of the data owner, auth represents the auditing authority of the data owner, and Norm represents the data normalization of the data owner.
3. The method for evaluating the reputation of data transactions based on an evaluation body according to claim 1, wherein said step 2 is specifically as follows:
trust is constrained by time, domain and environmental factors, entities are in different waysDifferent behavior capacities and transaction credibility are shown in the context environment of the system, so that transaction amount, transaction time and response time transaction context attribute indexes are designed, and after a data requesting party initiates a transaction request, an evaluation body monitors transaction context attributes and corresponding transaction amount factors：
；
Wherein,transaction amount factor representing the ith transaction of the data owner,/->Indicating the amount of the ith transaction,is the average sales of the data owners;
corresponding transaction amount time factor：
；
Wherein,transaction time factor representing the ith transaction of the data owner,/->Indicates the current date,/->Representation ofThe data owner obtains the reputation evaluation date of the ith transaction, the reputation score obtained by the seller in 7 consecutive days does not attenuate in time, and the reputation score is correspondingly set to be 1;
corresponding response time factor：
；
Wherein,response time factor representing the ith transaction of the data owner, +.>Time indicating that the data owner responded to the data requester transaction request,/->Representing the time when the data requester initiates the transaction application, the reputation score obtained by the seller within 1 hour is not subject to time decay, and is correspondingly set to be 1.
4. The method for evaluating the reputation of data transactions based on an evaluation body according to claim 1, wherein the step 3 is specifically as follows:
the method reduces the space complexity of reputation data retrieval of a data owner in data transaction, adopts a session mode to construct the association relation between the user and an evaluation body, is very useful in a system of a large-order user, and can avoid retrieving information in a huge dataset if the user performs reputation evaluation request in the session active state when the transaction occurs.
5. The method for evaluating the reputation of data transactions based on an evaluation body according to claim 1, wherein said step 4 is specifically as follows:
when a user initiates a reputation evaluation request, positioning an evaluation body through session, processing transaction data by the evaluation body to obtain a data quality index value, acquiring three reputation data of consistent description, service attitude and logistics service submitted by a data requesting party through a method, adopting five-level grading, and finally converting into three-level grading, wherein the value range is { -1,0,1}, forming the evaluation body, and finally calculating and updating the reputation value according to a reputation evaluation rule:
corresponding data quality factor：
；
Wherein,data quality factor representing the ith transaction of the data owner,/->An integrity indicator value indicative of the ith transaction, < +.>A singleness index value indicating the ith transaction, < + >>The data value representing the ith transaction is set to be 0.0009 when the value is smaller than the threshold value of 0.0009 in order to ensure the correctness of the calculation formula, the result after logarithmic operation is a value of a single digit level, and the change rate of the control reputation value is not too fast.
6. The method for evaluating the reputation of data transactions based on an evaluation body according to claim 1, wherein said step 5 is specifically as follows:
the data is used as commodity and has the characteristics of storage, easy replication and easy propagation, the data owner dishonest issues false data, the data requester cannot obtain fraud compensation through refund, and the fraud penalty factors need to satisfy the properties: (1) The data owner provides false data and is successfully reported by the data requester, and the data owner obtains fraud punishment; (2) The data owners can continuously issue false data, and the success of reporting by the data requesters can greatly reduce the credit score:
the reputation evaluation aggregation method comprises the following steps:
；
wherein,the score is represented by the score obtained at the ith transaction of the data owner at (1, 0, -1).
7. The method for evaluating the reputation of data transactions based on an evaluation body according to claim 1, wherein said step 6 is specifically as follows:
the fraud penalty factor is:
β=e -αx ；
wherein β represents a fraud penalty factor for adjusting the reputation value REP, a represents an adjustment parameter for the fraud penalty factor, a represents a value (0, 1), x represents a number of consecutive fraud, and represents a number of fraud occurrences in a fixed period of time, and when the ith transaction of DO is detected as a dummy transaction, the seller accepts the penalty rep=β.
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