Data transaction processing method based on intelligent management and control

Abstract

The invention discloses a data transaction processing system based on intelligent control, which comprises a data acquisition module, a transaction management module and a data management module, and is characterized in that the data acquisition module is used for collecting data uploaded by a user or crawling data on a public website through a crawler; the transaction management module is used for signing an intelligent contract by two data transaction parties and evaluating the risk of the two data transaction parties, the data management module is used for managing the access permission of data and the cache permission of the data, and the data acquisition module, the transaction management module and the data management module are electrically connected with each other. The data acquisition module comprises a login module, a data uploading module and a data crawling module. The device has the characteristics of high safety and high practicability.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a data transaction processing method based on intelligent management and control. Background Art

[0002] With the rapid development of Internet technology, the amount of network data has also exploded. Faced with massive amounts of data, how to deal with it has become a major problem, and the cost of data processing is getting higher and higher. There is also a risk of unavailability of processed data. Therefore, most companies prefer to trade data on data trading platforms. However, when the two parties to a data transaction conduct data transactions on a data trading platform, there is a possibility that the data buyer steals the data authorized by the data seller during the use of the data. In addition, in the existing technology, the data buyer cannot verify the data when purchasing the data, and cannot determine its authenticity, which may cause the data buyer to be deceived, and some data involving sensitive privacy may be leaked or used illegally. Therefore, it is necessary to design a data transaction processing method based on intelligent management and control that is highly secure and practical. Summary of the invention

[0003] The purpose of the present invention is to provide a data transaction processing method based on intelligent management and control to solve the problems raised in the above background technology.

[0004] In order to solve the above technical problems, the present invention provides the following technical solutions: a data transaction processing method based on intelligent management and control, including a data acquisition module, a transaction management module and a data management module, characterized in that: the data acquisition module is used to collect data uploaded by users or crawl data on public websites through crawlers, the transaction management module is used for both parties of data transactions to sign smart contracts and evaluate the risks of both parties of data transactions, the data management module is used to manage data access rights and data caching rights, and the data acquisition module, transaction management module and data management module are electrically connected to each other.

[0005] According to the above technical solution, the data acquisition module includes a login module, a data upload module and a data crawling module. The login module is used for users to log in to the data trading platform for data transactions, or for administrators to log in to the trading platform to manage the trading platform. The data upload module is used for users to upload data sold by the authorized platform to the platform database. The data crawling module is used to use a crawler to crawl authorized data on the web page.

[0006] According to the above technical solution, the transaction management module includes a data processing module, a risk assessment module, a user verification module and a smart contract module. The data processing module is used to pre-process the data uploaded by the user or the data crawled by the crawler. The risk assessment module is used by the platform to verify the authenticity, credibility and data value of the data. The smart contract module is used to protect the legitimate rights and interests of both parties to the transaction.

[0007] According to the above technical solution, the risk assessment module includes a data verification submodule, a reputation assessment submodule and a value assessment submodule. The data verification submodule is used by the data trading platform to verify the authenticity of the data uploaded by the user, the reputation assessment submodule is used by the data trading platform to assess the reputation level of the two parties to the transaction, and the value assessment submodule is used by the data trading platform to assess the value of the data uploaded by the data seller for sale.

[0008] According to the above technical solution, the data management module includes a rights management module, a data cache module and a user evaluation module. The rights management module is used by the data seller to manage the usage rights of the sold data, the data cache module is used by the data trading platform to cache the user purchased data, and the user evaluation module is used by the data buyer to evaluate and feedback the data after use.

[0009] According to the above technical solution, the operation method of the data transaction processing system includes the following steps:

[0010] Step S1: The user logs in to the data trading platform through the login module, establishes a personal database on the data trading platform, uploads the data to be sold to the personal database, establishes a system database, and stores the data crawled by the crawler in the system database;

[0011] Step S2: Scan the user uploaded data and crawled data, process the privacy data, and after the data is processed, the electrical signal starts the risk assessment module to start assessing the risk of false data collected this time. The data trading platform assesses the authenticity of the data product and the reputation of the data seller;

[0012] Step S3: Lock the data selected by the user, start the user verification module to verify the data, and sign the smart contract;

[0013] Step S4: After the user conducts a data transaction, the rights management module is started to obtain the smart contract signed by both parties to the transaction, restrict the use rights of the data according to the content of the smart contract, and the user gives an evaluation based on the usage experience.

[0014] According to the above technical solution, step S2 further includes the following steps:

[0015] Step S21: After data collection is completed, retrieve the data uploaded by the user in the personal database, identify the privacy data in the data, mark the data, and encrypt the marked data using a key;

[0016] Step S22: When the data processing is completed, the electrical signal triggers the risk assessment module, which retrieves the corresponding data records in the database based on the data introduction compiled by the user, and verifies the authenticity of the data uploaded by the user. By verifying the authenticity of the data uploaded by the user, the frequency of data fraud on the platform can be reduced.

[0017] According to the above technical solution, step S22 further includes the following steps:

[0018] Step S221: Start the data verification submodule, obtain the data uploaded by the user and the data usage description, randomly extract data fragments, extract the user's sales introduction of the data product, identify the semantics in the sales introduction to determine the purpose of the data product, use the integrated data to train the model according to the data usage description, and use the test sample to test the model. If the test result is consistent with the description of the sales introduction, the data product is authentic and valid, and the system will label the data as qualified. Otherwise, the data product is counterfeit, and the system will mark it with a false delisting label and delist the data product;

[0019] Step S222: retrieve the user's historical uploaded data, identify the label on each data product, and measure the number of false labels m and the total number of labels M. Whenever the reputation evaluation submodule identifies a false label, according to the formula Calculate the recommendation credit score that needs to be deducted, where i=1,2,3...n, θ represents the score that needs to be deducted, α represents the initial recommendation credit score, and calculate the remaining recommendation credit score. If it is less than one point, it will be counted as one point. When the credit score is less than the first threshold and greater than the second threshold, the system will cancel its privilege of priority recommendation based on the credit score. When the credit score is less than the third threshold, the system will close its data commodity sales function.

[0020] According to the above technical solution, step S3 further includes the following steps:

[0021] Step S31: Acquire the data selected by the data buyer, retrieve the historical sales records of the data, extract the sales price, and display the sales price on the client;

[0022] Step S32: The receipt purchaser clicks on the trial on the client, retrieves a data copy of the data product, encrypts the data copy and encapsulates the data in a trial plug-in, transmits the trial plug-in to the purchaser's client via a data line, and the user who purchased the data imports it into the system. The trial plug-in blocks the access path to its internal data. The seller remotely controls the trial plug-in to decrypt the data and injects it into the purchaser's system, and uses the system to verify the authenticity of the data. In the process of verifying the data, the trial plug-in is used to block all data access paths in the system;

[0023] Step S33: After the buyer confirms the purchase, the two parties to the data transaction sign a smart contract, which further specifies the transaction nature of the data product and sets the buyer's usage rights.

[0024] According to the above technical solution, step S4 further includes the following steps:

[0025] Step S41: Obtain the smart contract, identify the data transaction type on the smart contract, and manage the data usage rights according to the data transaction type. When the data product is purchased by the buyer, the rights management module will obtain the buyer's database, mirror the data sold by the data seller, and store it in the buyer's database;

[0026] Step S42: Further, when the data commodity is leased by the purchaser, the rights management module retrieves the number of leases or the duration in the smart contract, sets the lease plug-in usage period, compresses and stores the data in the lease plug-in, and the purchaser imports the plug-in into the system. The lease plug-in uses the data carrier to import the data copy into the system. When the data carrier detects an external data access request, the data carrier will generate an interference factor to make the access request lose the capture target. After the use is over, the data carrier will be automatically erased together with the data. After the lease period ends, the lease plug-in will close all access paths and destroy the data, thereby preventing the data from being stolen in the system.

[0027] Step S43: The user evaluates the data seller or other users through the user evaluation module. The system obtains the evaluation results, identifies and analyzes the evaluation, and determines the nature of the user evaluation. When a bad review appears, the data product is marked as a bad review, and the data seller is credit-scored through the above method. When a good review is judged, the system will restore some of its credit score.

[0028] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention enables the purchaser to verify the authenticity of the data commodity through the trial plug-in, and at the same time uses the trial plug-in to block all access paths of the system to prevent users who purchase data from taking the opportunity to access the system and extract data during the operation of the trial plug-in, thereby protecting the data from being stolen during the inspection process; by shutting down the data commodity sales function of the data seller, it is possible to limit the false sales behavior of unscrupulous vendors, reduce the emergence of false data commodities, and protect the legitimate rights and interests of receipt purchasers; through the verification of data commodities by the data trading platform, it is possible to avoid the phenomenon of illegal vendors peddling false data commodities, and protect the reputation of the data trading platform; and by releasing interference factors through data carriers, it is possible to allow data to escape capture by the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0030] Figure 1 It is a schematic diagram of the system module composition of the present invention. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0032] See also Figure 1 The present invention provides a technical solution: a data transaction processing method based on intelligent management and control, including a data acquisition module, a transaction management module and a data management module, characterized in that: the data acquisition module is used to collect data uploaded by users or crawl data on public websites through crawlers, the transaction management module is used for both parties of data transactions to sign smart contracts and evaluate the risks of both parties of data transactions, the data management module is used to manage data access rights and data cache rights, and the data acquisition module, the transaction management module and the data management module are electrically connected to each other.

[0033] The data collection module includes a login module, a data upload module and a data crawling module. The login module is used for users to log in to the data trading platform to conduct data transactions, or for administrators to log in to the trading platform to manage the trading platform. The data upload module is used for users to upload data sold by the authorized platform to the platform database. The data crawling module is used to use crawlers to crawl authorized data on web pages.

[0034] The transaction management module includes a data processing module, a risk assessment module, a user verification module and a smart contract module. The data processing module is used to pre-process the data uploaded by users or crawled by crawlers. The risk assessment module is used by the platform to verify the authenticity, credibility and data value of the data. The smart contract module is used to protect the legitimate rights and interests of both parties to the transaction.

[0035] The risk assessment module includes a data verification submodule, a reputation assessment submodule and a value assessment submodule. The data verification submodule is used by the data trading platform to verify the authenticity of the data uploaded by the user. The reputation assessment submodule is used by the data trading platform to assess the reputation level of both parties to the transaction. The value assessment submodule is used by the data trading platform to assess the value of the data uploaded by the data seller for sale.

[0036] The data management module includes a rights management module, a data cache module and a user evaluation module. The rights management module is used by the data seller to manage the usage rights of the sold data. The data cache module is used by the data trading platform to cache the user-purchased data. The user evaluation module is used by the data buyer to evaluate and provide feedback on the data after use.

[0037] The operation method of the data transaction processing system includes the following steps:

[0038] Step S1: The user logs in to the data trading platform through the login module, establishes a personal database on the data trading platform, uploads the data to be sold to the personal database, establishes a system database, and stores the data crawled by the crawler in the system database;

[0039] Step S2: Scan the user uploaded data and crawled data, process the privacy data, and after the data is processed, the electrical signal starts the risk assessment module to start assessing the risk of false data collected this time. The data trading platform assesses the authenticity of the data product and the reputation of the data seller;

[0040] Step S3: Lock the data selected by the user, start the user verification module to verify the data, and sign the smart contract;

[0041] Step S4: After the user conducts a data transaction, the rights management module is started to obtain the smart contract signed by both parties to the transaction, restrict the use rights of the data according to the content of the smart contract, and the user gives an evaluation based on the usage experience.

[0042] Step S2 further comprises the following steps:

[0043] Step S21: After data collection is completed, retrieve the data uploaded by the user in the personal database, identify the private data in the data, mark the data, and encrypt the marked data using a key, so as to prevent the data user from stealing the private data in the data and protect the privacy security of the user;

[0044] Step S22: When the data processing is completed, the electrical signal triggers the risk assessment module, which retrieves the corresponding data records in the database based on the data introduction compiled by the user, and verifies the authenticity of the data uploaded by the user. By verifying the authenticity of the data uploaded by the user, the frequency of data fraud on the platform can be reduced.

[0045] Step S22 further includes the following steps:

[0046] Step S221: Start the data verification submodule, obtain the data uploaded by the user and the data usage description, randomly extract data fragments, extract the user's sales introduction of the data product, identify the semantics in the sales introduction to determine the purpose of the data product, use the integrated data training model according to the data usage description, and use the test sample to test the model. If the test result is consistent with the description of the sales introduction, the data product is authentic and valid, and the system will attach a qualified label to the data, indicating that the data inspection is qualified. Otherwise, the data product is counterfeit, and the system will mark it with a false removal label, indicating that the data inspection is unqualified, and the data product will be removed from the shelves. The verification of data products by the data trading platform can avoid the phenomenon of illegal vendors selling false data products, and can protect the reputation of the data trading platform;

[0047] Step S222: retrieve the user's historical uploaded data, identify the label on each data product, and measure the number of false labels m and the total number of labels M. Whenever the reputation evaluation submodule identifies a false label, according to the formula Calculate the recommendation credit score that needs to be deducted, where i=1,2,3...n, θ represents the score that needs to be deducted, α represents the initial recommendation credit score, and calculate the remaining recommendation credit score. If it is less than one point, it will be counted as one point. When the credit score is less than the first threshold and greater than the second threshold, the system will cancel its privilege of priority recommendation based on the credit score. By canceling the privilege of priority recommendation, the data products sold by the seller will no longer participate in the ranking recommendation based on the value of the credit score. When the credit score is less than the third threshold, the system will close its data product sales function. By closing the data product sales function of the data seller, it can limit the false sales behavior of unscrupulous vendors, reduce the appearance of false data products, and protect the legitimate rights and interests of receipt buyers.

[0048] Step S3 further comprises the following steps:

[0049] Step S31: Obtain the data selected by the data buyer, retrieve the historical sales records of the data, extract the sales price, and display the sales price on the client, so that the user who purchases the data can understand the market situation of the data product and reduce the risk of being cheated;

[0050] Step S32: The receipt purchaser clicks on the trial on the client, retrieves a data copy of the data product, encrypts the data copy and encapsulates the data in a trial plug-in. Encryption can prevent the user who purchased the data product from cracking the trial plug-in and stealing the data therein. The trial plug-in is transmitted to the purchaser's client through a data line. The user who purchased the data imports it into the system. The trial plug-in blocks the access path to its internal data to prevent the user who purchased the data from accessing the plug-in and stealing the data during the process of decrypting the data by the trial plug-in. The seller remotely controls the trial plug-in to decrypt the data and injects it into the purchaser's system. The system is used to verify the authenticity of the data. During the data verification process, the trial plug-in is used to block all data access paths of the system to prevent the user who purchased the data from taking the opportunity to access the system and extract data from it during the working period of the trial plug-in. The purchaser can verify the authenticity of the data product through the trial plug-in. At the same time, the trial plug-in is used to block all access paths of the system to protect the data from being stolen during the inspection process.

[0051] Step S33: After the purchaser confirms the purchase, the two parties to the data transaction sign a smart contract, which further specifies the transaction nature of the data commodity and sets the purchaser's usage rights. The smart contract restricts the purchaser from using the data at will, thereby protecting the legitimate rights and interests of the data commodity holders.

[0052] Step S4 further comprises the following steps:

[0053] Step S41: Obtain the smart contract, identify the data transaction type on the smart contract, and manage the data usage rights according to the data transaction type. When the data commodity is purchased by the buyer, the rights management module will obtain the buyer's database, mirror the data sold by the data seller and store it in the buyer's database. By mirroring the data, it is possible to avoid data incompleteness during transmission and cause disputes;

[0054] Step S42: Further, when the data commodity is leased by the purchaser, the authority management module retrieves the number of leases or the duration in the smart contract, and sets the lease plug-in usage cycle. By setting the usage cycle for the lease plug-in, the data can be destroyed in time at the end of the lease period, so as to avoid data leakage caused by attacks on the data carrier. The data is compressed and stored in the lease plug-in. The purchaser imports the plug-in into the system. The lease plug-in imports the data copy into the system using the data carrier. By importing the data copy into the system, the data entering the system can be cleared without damaging the original data. When the data carrier detects an external data access request, the data carrier will generate an interference factor, so that the access request loses the capture target. By releasing the interference factor through the data carrier, the data can escape the capture of the system. After the use, the data carrier will be automatically erased together with the data. After the lease period ends, the lease plug-in will close all access paths and destroy the data, so that the data will not be stolen in the system. By using the lease plug-in to control the data carrier to clear the data, the data can be prevented from remaining in the system and being stolen, which greatly improves the security of the data.

[0055] Step S43: The user evaluates the data seller or other users through the user evaluation module. The system obtains the evaluation results, identifies and analyzes the evaluation, and determines the nature of the user evaluation. When a bad review appears, the data product is marked as a bad review, and the data seller is credit-scored through the above method. When a good review is judged, the system will restore some of its credit score.

[0056] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0057] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A data transaction processing system based on intelligent management and control, including a data acquisition module, a transaction management module and a data management module, characterized in that: The data acquisition module is used to collect data uploaded by users or crawl data on public websites through crawlers. The transaction management module is used for both parties of data transactions to sign smart contracts and evaluate the risks of both parties of data transactions. The data management module is used to manage data access permissions and data caching permissions. The data acquisition module, transaction management module and data management module are electrically connected to each other.

2. The data transaction processing system based on intelligent management and control according to claim 1 is characterized in that: The data collection module includes a login module, a data upload module and a data crawling module. The login module is used for users to log in to the data trading platform to conduct data transactions, or for administrators to log in to the trading platform to manage the trading platform. The data upload module is used for users to upload data sold by the authorized platform to the platform database. The data crawling module is used to use a crawler to crawl authorized data on the web page.

3. The data transaction processing system based on intelligent management and control according to claim 2 is characterized in that: The transaction management module includes a data processing module, a risk assessment module, a user verification module and a smart contract module. The data processing module is used to pre-process the data uploaded by the user or the data crawled by the crawler. The risk assessment module is used for the platform to verify the authenticity, reputation level and data value of the data. The smart contract module is used to protect the legitimate rights and interests of both parties to the transaction.

4. The data transaction processing system based on intelligent management and control according to claim 3 is characterized in that: The risk assessment module includes a data verification submodule, a reputation assessment submodule and a value assessment submodule. The data verification submodule is used by the data trading platform to verify the authenticity of the data uploaded by the user, the reputation assessment submodule is used by the data trading platform to assess the reputation level of the two parties to the transaction, and the value assessment submodule is used by the data trading platform to assess the value of the data uploaded by the data seller for sale.

5. The data transaction processing system based on intelligent management and control according to claim 4 is characterized in that: The data management module includes a rights management module, a data cache module and a user evaluation module. The rights management module is used by the data seller to manage the usage rights of the sold data, the data cache module is used by the data trading platform to cache the user purchased data, and the user evaluation module is used by the data buyer to evaluate and feedback the data after use.

6. The data transaction processing method based on intelligent management and control according to claim 5 is characterized in that: The operation method of the data transaction processing system comprises the following steps: Step S1: The user logs in to the data trading platform through the login module, establishes a personal database on the data trading platform, uploads the data to be sold to the personal database, establishes a system database, and stores the data crawled by the crawler in the system database; Step S2: Scan the user uploaded data and crawled data, process the privacy data, and after the data is processed, the electrical signal starts the risk assessment module to start assessing the risk of false data collected this time. The data trading platform assesses the authenticity of the data product and the reputation of the data seller; Step S3: Lock the data selected by the user, start the user verification module to verify the data, and sign the smart contract; Step S4: After the user conducts a data transaction, the rights management module is started to obtain the smart contract signed by both parties to the transaction, restrict the use rights of the data according to the content of the smart contract, and the user gives an evaluation based on the usage experience.

7. The data transaction processing method based on intelligent management and control according to claim 6 is characterized in that: The step S2 further comprises the following steps: Step S21: After data collection is completed, retrieve the data uploaded by the user in the personal database, identify the privacy data in the data, mark the data, and encrypt the marked data using a key; Step S22: When the data processing is completed, the electrical signal triggers the risk assessment module, which retrieves the corresponding data records in the database based on the data introduction compiled by the user, and verifies the authenticity of the data uploaded by the user. By verifying the authenticity of the data uploaded by the user, the frequency of data fraud on the platform can be reduced.

8. The data transaction processing method based on intelligent management and control according to claim 7 is characterized in that: The step S22 further comprises the following steps: Step S221: Start the data verification submodule, obtain the data uploaded by the user and the data usage description, randomly extract data fragments, extract the user's sales introduction of the data product, identify the semantics in the sales introduction to determine the purpose of the data product, use the integrated data to train the model according to the data usage description, and use the test sample to test the model. If the test result is consistent with the description of the sales introduction, the data product is authentic and valid, and the system will label the data as qualified. Otherwise, the data product is counterfeit, and the system will mark it with a false delisting label and delist the data product; Step S222: retrieve the user's historical uploaded data, identify the label on each data product, and measure the number of false labels m and the total number of labels M. Whenever the reputation evaluation submodule identifies a false label, according to the formula Calculate the recommendation credit score that needs to be deducted, where i=1,2,3...n, θ represents the score that needs to be deducted, α represents the initial recommendation credit score, and calculate the remaining recommendation credit score. If it is less than one point, it will be counted as one point. When the credit score is less than the first threshold and greater than the second threshold, the system will cancel its privilege of priority recommendation based on the credit score. When the credit score is less than the third threshold, the system will close its data commodity sales function.

9. The data transaction processing method based on intelligent management and control according to claim 8 is characterized in that: The step S3 further comprises the following steps: Step S31: Acquire the data selected by the data buyer, retrieve the historical sales records of the data, extract the sales price, and display the sales price on the client; Step S32: The receipt purchaser clicks on the trial on the client, retrieves a data copy of the data product, encrypts the data copy and encapsulates the data in a trial plug-in, transmits the trial plug-in to the purchaser's client via a data line, and the user who purchased the data imports it into the system. The trial plug-in blocks the access path to its internal data. The seller remotely controls the trial plug-in to decrypt the data and injects it into the purchaser's system, and uses the system to verify the authenticity of the data. In the process of verifying the data, the trial plug-in is used to block all data access paths in the system; Step S33: After the buyer confirms the purchase, the two parties to the data transaction sign a smart contract, which further specifies the transaction nature of the data product and sets the buyer's usage rights.

10. The data transaction processing method based on intelligent management and control according to claim 9 is characterized in that: The step S4 further comprises the following steps: Step S41: Obtain the smart contract, identify the data transaction type on the smart contract, and manage the data usage rights according to the data transaction type. When the data product is purchased by the buyer, the rights management module will obtain the buyer's database, mirror the data sold by the data seller, and store it in the buyer's database; Step S42: Further, when the data commodity is leased by the purchaser, the rights management module retrieves the number of leases or the duration in the smart contract, sets the lease plug-in usage period, compresses and stores the data in the lease plug-in, and the purchaser imports the plug-in into the system. The lease plug-in uses the data carrier to import the data copy into the system. When the data carrier detects an external data access request, the data carrier will generate an interference factor to make the access request lose the capture target. After the use is over, the data carrier will be automatically erased together with the data. After the lease period ends, the lease plug-in will close all access paths and destroy the data, thereby preventing the data from being stolen in the system. Step S43: The user evaluates the data seller or other users through the user evaluation module. The system obtains the evaluation results, identifies and analyzes the evaluation, and determines the nature of the user evaluation. When a bad review appears, the data product is marked as a bad review, and the data seller is credit-scored through the above method. When a good review is judged, the system will restore some of its credit score.

Images