CN114003954A - User evaluation management method, system, computer device and storage medium - Google Patents
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Abstract
The invention provides a user evaluation management method, a user evaluation management system, computer equipment and a storage medium, wherein the method comprises the following steps: storing the user comment content into a local database and acquiring a local storage address of the user comment content; carrying out hash coding on the comment content to obtain a comment content abstract, and uploading the comment content abstract and a comment content local storage address to a block chain; extracting a summary of the comment content to be checked and a local storage address from the block chain, and reading the comment content from a local database according to the local storage address of the comment content to be checked; and performing Hash coding on the comment content read from the local database, comparing the coding result with the to-be-checked comment content abstract extracted from the block chain, and if the coding result is consistent with the to-be-checked comment content abstract, judging that the comment content is not tampered. According to the technical scheme provided by the invention, only the review content abstract is linked, the real consumption feedback of the user is recorded, the block chain storage space occupation can be avoided, and the problem that the user evaluation management is lacked in the prior art is solved.
Description
Technical Field
The present invention relates to the field of internet transaction technologies, and in particular, to a user evaluation management method, a user evaluation management system, a computer device, and a computer-readable storage medium.
Background
With the development of internet technology, the internet transaction coverage is more and more extensive. The buyer and the seller mainly carry out transaction activities through the Internet transaction platform, and the Internet transaction does not have a traditional physical storefront, so that the operation cost can be controlled better compared with a traditional transaction mode, and the price advantage is higher. With the increasing number of online sellers, the user has no physical reference when purchasing, so that the dependence on commodity evaluation is high.
However, the existing internet transaction platform mainly analyzes user behaviors such as user consumption behaviors, search behaviors, browsing behaviors and the like, aims to recommend commodities to users, and lacks management on user evaluation, so that merchants can easily tamper with user comments, and consumers are difficult to obtain real user consumption feedback.
Disclosure of Invention
The present invention has been made, at least in part, to solve the technical problem of the prior art that the business can easily tamper with the user comment, which is lack of management of user evaluation.
According to an aspect of the present invention, there is provided a user evaluation management method, including:
storing the user comment content into a local database and acquiring a local storage address of the user comment content;
carrying out Hash coding on the comment content to obtain a comment content abstract, and uploading the comment content abstract and a comment content local storage address to a block chain;
extracting a summary of the comment content to be checked from the block chain, obtaining a local storage address of the comment content to be checked, and reading the comment content from a local database according to the local storage address of the comment content to be checked;
and performing Hash coding on the comment content read from the local database, comparing the coding result with the to-be-checked comment content abstract extracted from the block chain, and if the coding result is consistent with the to-be-checked comment content abstract, judging that the to-be-checked comment content is not tampered.
Optionally, the method further comprises:
when a user adds comments to the same consumption, storing the added comment contents into a local database according to the local storage address of the original comment contents;
and carrying out hash coding on the original comment content and the additional comment content together to obtain an updated comment content abstract, and updating in the block chain.
Optionally, obtaining a local storage address of the comment content to be checked from the blockchain includes:
calling a block chain gateway interface;
calling an intelligent contract query method;
and inquiring the summary of the comment content to be checked based on an intelligent contract inquiry method in the block chain to obtain a local storage address of the comment content to be checked.
Optionally, before storing the user comment content in the local database, the method further includes:
after the user carries out consumption evaluation, judging whether the user is abnormal consumption comment according to the current comment content of the user;
if so, marking the current comment of the user as an abnormal consumption comment, marking the user as an abnormal user, storing the current comment content of the user and the mark of the current comment content into a local database, and acquiring a local storage address of the local database.
Optionally, the determining, according to the current comment content of the user, whether the comment is an abnormal consumption comment specifically includes:
and performing similarity analysis on the current comment content of the user based on the historical comment content of the current commodity and the historical comment content of the commodity with the same type and the same price, and judging whether the comment content is an abnormal consumption comment or not according to an analysis result.
Optionally, performing similarity analysis on the current comment content of the user based on the historical comment content of the current commodity and the historical comment content of the commodity with the same price and judging whether the current comment content of the user is an abnormal consumption comment according to an analysis result, including:
acquiring current comment content of a user;
judging whether text comments exist in the current comment content of the user, if so, carrying out feature vector coding on the text comments, the historical comment content of the current commodity and the text information in the historical comment content of the commodity with the same type and the same price to obtain respective corresponding text feature vectors;
calculating first cosine similarity of text feature vectors of current comment contents of users and text feature vectors of historical comment contents of current commodities and historical comment contents of commodities with the same type and the same price;
and judging whether the first cosine similarity is larger than a first preset threshold value, and if so, considering that the current comment of the user is an abnormal consumption comment.
Optionally, after obtaining the current comment content of the user, the method further includes:
judging whether a picture comment exists in the current comment content of the user, if so, carrying out feature vector coding on the picture comment, the historical comment content of the current commodity and the picture information in the historical comment content of the same-type commodity to obtain respective corresponding image feature vectors;
calculating second cosine similarity of the image feature vector of the current comment content of the user and the image feature vectors of the historical comment content of the current commodity and the historical comment content of the commodity with the same type and the same price;
and judging whether the second cosine similarity is larger than a second preset threshold, and if so, considering that the current comment of the user is an abnormal consumption comment.
According to another aspect of the present invention, there is provided a user evaluation management system including:
the storage module is used for storing the user comment content into a local database and acquiring a local storage address of the user comment content;
the encoding module is used for carrying out Hash encoding on the comment content to obtain a comment content abstract;
the uploading module is used for uploading the comment content abstract and the comment content local storage address to the block chain;
the extraction module is used for extracting the summary of the comment content to be checked from the block chain, obtaining the local storage address of the comment content to be checked, and reading the comment content from the local database according to the local storage address of the comment content to be checked;
the encoding module is also configured to perform hash encoding on the comment content read from the local database; and the number of the first and second groups,
and the comparison module is used for comparing the encoding result of the comment content read from the local database with the to-be-checked comment content abstract extracted from the block chain, and if the encoding result of the comment content read from the local database is consistent with the to-be-checked comment content abstract, judging that the to-be-checked comment content is not tampered.
According to still another aspect of the present invention, there is provided a computer apparatus including a memory in which a computer program is stored and a processor that executes the aforementioned user evaluation management method when the processor runs the computer program stored in the memory.
According to yet another aspect of the present invention, there is provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, executes the aforementioned user evaluation management method.
The technical scheme provided by the invention can have the following beneficial effects:
according to the user evaluation management method provided by the invention, the block chain technology is applied to the user evaluation supervision of the internet transaction platform, the comment content is stored in the local database after the hash coding is carried out on the comment content of the user, and only the comment content abstract and the local storage address are subjected to chain link recording, so that whether the comment content is falsified can be judged, the comment content can not be deleted and modified, the real consumption feedback of the user is recorded, the block chain storage space is prevented from being occupied, the transaction timeliness of the chain is improved, and the transaction delay is reduced.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the example serve to explain the principles of the invention and not to limit the invention.
Fig. 1 is a schematic flow chart of a user evaluation management method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a user evaluation analysis and evaluation uplink process according to an embodiment of the present invention;
fig. 3 is a schematic view of a user evaluation processing flow provided in the embodiment of the present invention;
fig. 4 is a schematic structural diagram of a user evaluation management system according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a computer device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the following detailed description of the embodiments of the present invention is provided with reference to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the present invention, are given by way of illustration and explanation only, not limitation.
In the following description, suffixes such as "module", "component", or "unit" used to denote elements are used only for facilitating the explanation of the present invention, and have no specific meaning in itself. Thus, "module", "component" or "unit" may be used mixedly; furthermore, the embodiments and features of the embodiments of the present invention may be arbitrarily combined with each other without conflict.
Fig. 1 is a schematic flow chart of a user evaluation management method according to an embodiment of the present invention. As shown in fig. 1, the method includes the following steps S101 to S104.
S101, storing the user comment content into a local database and acquiring a local storage address of the user comment content.
And S102, carrying out Hash coding on the comment content to obtain a comment content abstract, and uploading the comment content abstract and a comment content local storage address to a block chain.
In the step, the SHA256 algorithm can be used for carrying out hash coding on the comment content to generate a character string with a certain length so as to form a unique identifier of the comment content; the review content abstract and the corresponding local storage address of the review content can be stored in a newly-built table, and then the table is linked.
S103, extracting the summary of the comment content to be checked from the block chain, obtaining a local storage address of the comment content to be checked, and reading the comment content from the local database according to the local storage address of the comment content to be checked.
And S104, carrying out Hash coding on the comment content read from the local database, comparing the coding result with the to-be-checked comment content abstract extracted from the block chain, and if the coding result is consistent with the to-be-checked comment content abstract, judging that the to-be-checked comment content is not tampered. Of course, if the two are not consistent, it can be determined that the comment content to be checked has been tampered.
In this step, the SHA256 algorithm may also be used to perform hash coding on the comment content read from the local database, and generate a character string with a certain length, so as to form a unique identifier of the comment content read from the local database.
In the embodiment, the blockchain technology is applied to user evaluation supervision of an internet transaction platform (also called an e-commerce platform), after hash coding is carried out on user comment contents, the comment contents are stored in a local database, and only the comment content abstract and the local storage address are subjected to chain link recording, so that whether the comment contents are tampered or not can be judged, undeletable modification of the comment contents is realized, real consumption feedback of users is recorded, the blockchain storage space is prevented from being occupied, the transaction timeliness of a chain is improved, and the transaction delay is reduced.
In a specific embodiment, the method further includes the following steps S105 and S106.
S105, when the user adds comments to the same consumption, storing the added comment contents into a local database according to the local storage address of the original comment contents;
and S106, carrying out Hash coding on the original comment content and the additional comment content together to obtain an updated comment content abstract, and updating in the block chain.
Of course, the updated content is only the summary of the comment content, and the local storage address of the comment content does not need to be updated because the local storage address of the comment content is unchanged.
In the embodiment, besides that the comment content can not be deleted and modified, the user evaluation can be added, the original comment content and the added comment content are subjected to hash coding and then are updated in the block chain, and the real consumption feedback of the user is further recorded.
In one embodiment, the obtaining of the local storage address of the comment content to be checked from the blockchain in step S103 includes the following steps a to c:
a. calling a block chain gateway interface;
b. calling an intelligent contract query method;
c. and inquiring the summary of the comment content to be checked based on an intelligent contract inquiry method in the block chain to obtain a local storage address of the comment content to be checked.
In this embodiment, the local storage address of the comment content to be checked can be obtained by querying the summary of the comment content to be checked in the block chain.
In a specific embodiment, before step S101, the following steps S107 and S108 are further included.
S107, after the user carries out consumption evaluation, judging whether the user is an abnormal consumption comment according to the current comment content of the user, if so, executing a step S108; if not, executing the step S101, directly storing the current comment content of the user into a local database and acquiring a local storage address of the comment content;
s108, marking the current comment of the user as an abnormal consumption comment, marking the user as an abnormal user, storing the current comment content of the user and the mark of the current comment content into a local database, acquiring a local storage address of the local database, and then directly executing the step S102.
In step S108, after the current comment of the user is marked as an abnormal consumption comment, the user may be directly marked as an abnormal user, or a number limit value may be set, and when the number of the abnormal consumption comments of the user is accumulated to the number limit value, the user is marked as an abnormal user.
The user needs to log in the consumption platform, commodity transaction is generated on the consumption platform, consumption evaluation can be carried out after consumption is completed, then analysis processing is carried out on the user evaluation to mark abnormal consumption evaluation and abnormal users, processed comment contents are stored in a local database, abstract extraction and abstract chain linking are carried out on the user evaluation contents through a Hash algorithm, the user can also carry out additional comment on the same consumption, and the series of operation flows are shown in detail in figure 2.
In the embodiment, the internet transaction platform only needs to add a data analysis work before storing the user comment content, judge whether the user current comment is a normal consumption comment, mark the user and the current comment according to the judgment result, store the user current comment content in the local database (if the user current comment content has a mark, store the mark together with the mark in the local database), link the comment content abstract and the comment content local storage address, namely store the comment content abstract and the comment content local storage address on the link, and analyze and store the user comment content under the link, so that the transaction timeliness of the link can be improved, and the transaction delay is reduced; in the process, an internet transaction platform and an application access block chain are not needed, the flow is simplified, and the processing efficiency is improved.
In a specific embodiment, step S107 specifically includes: and performing similarity analysis on the current comment content of the user based on the historical comment content of the current commodity and the historical comment content of the commodity with the same type and the same price, and judging whether the comment content is an abnormal consumption comment or not according to an analysis result.
The current commodity refers to a commodity which is currently subjected to consumption evaluation by a user; the same type of commodity with the same price refers to the commodity with the same type and the similar price as the current commodity.
In the embodiment, the historical comment content of the current commodity is used as a main basis, the historical comment content of the commodity with the same type and the same price is used as an auxiliary basis, similarity analysis is performed on the current comment content of the user, and then abnormal consumption comments and abnormal users are marked.
Further, as shown in fig. 3, the step S107 specifically includes the following steps S107a to S107e.
S107a, acquiring current comment content of a user;
s107b, judging whether the current comment content of the user has a text comment or not, if so, executing the step S107 c;
s107c, carrying out feature vector coding on text comments, historical comment contents of current commodities and text information in historical comment contents of same-type equivalent commodities to obtain respective corresponding text feature vectors;
s107d, calculating the first cosine similarity of the text feature vector of the current comment content of the user and the text feature vectors of the historical comment content of the current commodity and the historical comment content of the same-type commodity;
s107e, judging whether the first cosine similarity is larger than a first preset threshold value or not, and if so, considering that the current comment of the user is an abnormal consumption comment.
The first preset threshold value can be set and adjusted by a person skilled in the art according to actual conditions. For example, the first preset threshold may be 0.75.
When the cosine similarity of the text feature vector of the current comment content of the user and a certain historical comment content exceeds a first preset threshold value, the comment is judged to be an abnormal consumption comment, the current comment of the user is marked as an abnormal consumption comment, and the user is marked as an abnormal user.
In practical applications, there may be picture comments in addition to text comments, so, in a specific embodiment, as shown in fig. 3, after step S107a, step S107 further includes the following steps S107f to S107i.
S107f, judging whether the current comment content of the user has a picture comment or not, if so, executing the step S107 g;
s107g, carrying out feature vector coding on picture comments and picture information in historical comment contents of current commodities and historical comment contents of same-type and same-price commodities to obtain respective corresponding image feature vectors;
s107h, calculating second cosine similarity of the image feature vector of the current comment content of the user and the image feature vectors of the historical comment content of the current commodity and the historical comment content of the same-type commodity;
and S107i, judging whether the second cosine similarity is larger than a second preset threshold value, and if so, considering that the current comment of the user is an abnormal consumption comment.
The second preset threshold can be set and adjusted by those skilled in the art according to actual conditions. For example, the second preset threshold may be 0.85.
In the foregoing steps, if the determination in step S107b is no, step S107f may be executed; if the determination in step S107f is NO, step S107b may be performed.
When the cosine similarity of the image feature vector of the current comment content of the user and a certain historical comment content exceeds a second preset threshold value, the comment is judged to be an abnormal consumption comment, the current comment of the user is marked as an abnormal consumption comment, and the user is marked as an abnormal user.
The cosine similarity is calculated by the following formula:
a represents a text feature vector/an image feature vector of the current comment content of a user; b represents a text feature vector/image feature vector of a certain historical comment content of the current commodity or a certain historical comment content of the commodity with the same price.
In this embodiment, before the user comment content summary links up, the user comment content needs to be processed, abnormal consumption comments such as a bill swiped and the like are marked through text/picture similarity analysis, and a user who sends the abnormal consumption comments is marked, so that a consumer can further measure the commodity value through the marked consumption evaluation and the user, and a consumption decision is not misled.
It should be noted that the sequence of the above steps is only a specific example provided for illustrating the embodiment of the present invention, and the present invention does not limit the sequence of the above steps, and those skilled in the art can adjust the sequence as required in practical application; and the sequence number of the steps does not limit the execution sequence.
According to the user evaluation management method provided by the embodiment of the invention, a block chain technology is applied to user evaluation supervision of an internet transaction platform, the block chain can ensure the traceable and non-falsifiable characteristics of information by using a hash chain storage structure, and decentralization is realized by using a distributed storage form and a consensus mechanism of the block chain; on the other hand, the evaluation content of the user is analyzed, abnormal consumption comments and abnormal users are marked, the good comment of the bill brushing and the bill brushing user can be identified, and further valuable reference is provided for the consumer.
Fig. 4 is a schematic structural diagram of a user evaluation management system according to an embodiment of the present invention. As shown in fig. 4, the system 4 includes: a storage module 41, an encoding module 42, an uploading module 43, an extraction module 44, and a comparison module 45.
Wherein, the storage module 41 is configured to store the user comment content in a local database and obtain a local storage address thereof; the encoding module 42 is configured to perform hash encoding on the comment content to obtain a comment content abstract; the uploading module 43 is configured to upload the review content summary and the review content local storage address to the blockchain; the extraction module 44 is configured to extract the summary of the comment content to be checked from the block chain, obtain a local storage address of the comment content to be checked, and read the comment content from the local database according to the local storage address of the comment content to be checked; the encoding module 42 is further configured to hash the comment content read from the local database; the comparison module 45 is configured to compare the encoding result of the comment content read from the local database with the summary of the comment content to be checked extracted from the block chain, and if the encoding result of the comment content is consistent with the summary of the comment content to be checked, it is determined that the comment content to be checked is not tampered.
In the embodiment, the blockchain technology is applied to user evaluation supervision of an internet transaction platform (also called an e-commerce platform), after hash coding is carried out on user comment contents, the comment contents are stored in a local database, and only the comment content abstract and the local storage address are subjected to chain link recording, so that whether the comment contents are tampered or not can be judged, undeletable modification of the comment contents is realized, real consumption feedback of users is recorded, the blockchain storage space is prevented from being occupied, the transaction timeliness of a chain is improved, and the transaction delay is reduced.
In a specific embodiment, the storage module 41 is further configured to, when the user adds additional comments to the same consumption, store the additional comment content in the local database according to the local storage address of the original comment content; the encoding module 42 is further configured to perform hash encoding on the original comment content and the additional comment content together to obtain an updated comment content abstract; the upload module 43 is further configured to upload the updated review content summary into the blockchain and replace the original review content summary to complete the summary update.
In the embodiment, besides that the comment content can not be deleted and modified, the user evaluation can be added, the original comment content and the added comment content are subjected to hash coding and then updated in the block chain, and the real consumption feedback of the user is further recorded.
In one embodiment, the extraction module 44 includes: a calling unit and a query unit.
The calling unit is set to call a block chain gateway interface and call an intelligent contract query method; the query unit is set to query the summary of the comment content to be checked based on an intelligent contract query method in the block chain, and a local storage address of the comment content to be checked is obtained.
In this embodiment, the local storage address of the comment content to be checked can be obtained by querying the summary of the comment content to be checked in the block chain.
In one embodiment, the system 4 further comprises: the device comprises a judging module and a marking module.
The judging module is set to judge whether the user is an abnormal consumption comment according to the current comment content of the user after the user performs consumption evaluation. The marking module is set to mark the current comment of the user as the abnormal consumption comment and mark the user as the abnormal user when the judgment result of the judging module is the abnormal consumption comment.
Correspondingly, the storage module 41 is further configured to, when the judgment result of the judgment module is normal consumption comment, directly store the current comment content of the user in the local database and obtain the local storage address of the user; and when the judgment result of the judgment module is abnormal consumption comment, storing the current comment content of the user and the mark of the comment content into a local database and acquiring a local storage address of the comment content.
After the marking module marks the current comment of the user as the abnormal consumption comment, the user can be directly marked as the abnormal user, a number limit value can also be set, and when the number of the abnormal consumption comment of the user is accumulated to the number limit value, the user is marked as the abnormal user.
In the embodiment, the internet transaction platform only needs to add a data analysis work before storing the user comment content, judge whether the user current comment is a normal consumption comment, mark the user and the current comment according to the judgment result, store the user current comment content in the local database (if the user current comment content has a mark, store the mark together with the mark in the local database), link the comment content abstract and the comment content local storage address, namely store the comment content abstract and the comment content local storage address on the link, and analyze and store the user comment content under the link, so that the transaction timeliness of the link can be improved, and the transaction delay is reduced; in the process, an internet transaction platform and an application access block chain are not needed, the flow is simplified, and the processing efficiency is improved.
In a specific implementation manner, the judging module is specifically configured to perform similarity analysis on the current comment content of the user based on the historical comment content of the current commodity and the historical comment content of the commodity with the same type and the same price, and judge whether the comment is an abnormal consumption comment according to an analysis result.
The current commodity refers to a commodity which is currently subjected to consumption evaluation by a user; the same type of commodity with the same price refers to the commodity with the same type and the similar price as the current commodity.
In the embodiment, the historical comment content of the current commodity is used as a main basis, the historical comment content of the commodity with the same type and the same price is used as an auxiliary basis, similarity analysis is performed on the current comment content of the user, and then abnormal consumption comments and abnormal users are marked.
Further, the judging module comprises: the device comprises an acquisition unit, a first judgment unit, a first coding unit, a first calculation unit and a second judgment unit.
The obtaining unit is used for obtaining the current comment content of the user; the first judging unit is used for judging whether text comments exist in the current comment content of the user or not; the first encoding unit is set to encode the feature vectors of the text comments, the historical comment contents of the current commodity and the text information in the historical comment contents of the commodity with the same type and the same price to obtain the corresponding text feature vectors when the judgment result of the first judging unit is that the text comments exist; the first calculating unit is used for calculating the first cosine similarity of the text feature vector of the current comment content of the user and the text feature vectors of the historical comment content of the current commodity and the historical comment content of the same-type commodity; the second judging unit is set to judge whether the first cosine similarity is larger than a first preset threshold value, and if so, the current comment of the user is considered to be an abnormal consumption comment.
The first preset threshold value can be set and adjusted by a person skilled in the art according to actual conditions. For example, the first preset threshold may be 0.75.
When the cosine similarity of the text feature vector of the current comment content of the user and a certain historical comment content exceeds a first preset threshold value, the comment is judged to be an abnormal consumption comment, the current comment of the user is marked as an abnormal consumption comment, and the user is marked as an abnormal user.
In practical applications, besides text comments, there may be image comments, so the determining module further includes: the device comprises a third judging unit, a second coding unit, a second calculating unit and a fourth judging unit.
The third judging unit is used for judging whether the current comment content of the user has a picture comment or not; the second coding unit is arranged to perform feature vector coding on the picture comment and picture information in the historical comment content of the current commodity and the historical comment content of the commodity with the same type and the same price to obtain image feature vectors corresponding to the picture comment and the historical comment content when the picture comment exists in the judgment result of the third judgment unit; the second calculating unit is used for calculating the second cosine similarity of the image feature vector of the current comment content of the user and the image feature vectors of the historical comment content of the current commodity and the historical comment content of the same-type commodity; the fourth judging unit is set to judge whether the second cosine similarity is larger than a second preset threshold value, and if so, the current comment of the user is considered to be an abnormal consumption comment.
When the cosine similarity of the image feature vector of the current comment content of the user and a certain historical comment content exceeds a second preset threshold value, the comment is judged to be an abnormal consumption comment, the current comment of the user is marked as an abnormal consumption comment, and the user is marked as an abnormal user.
The cosine similarity is calculated by the following formula:
a represents a text feature vector/an image feature vector of the current comment content of a user; b represents a text feature vector/image feature vector of a certain historical comment content of the current commodity or a certain historical comment content of the commodity with the same price.
In this embodiment, before the user comment content summary links up, the user comment content needs to be processed, abnormal consumption comments such as a bill swiped and the like are marked through text/picture similarity analysis, and a user who sends the abnormal consumption comments is marked, so that a consumer can further measure the commodity value through the marked evaluation and the user, and a consumption decision is not misled.
According to the user evaluation management system provided by the embodiment of the invention, a block chain technology is applied to user evaluation supervision of an internet transaction platform, a block chain can ensure the traceable and non-falsifiable characteristics of information by using a hash chain storage structure, and decentralization is realized by using a distributed storage form and a consensus mechanism of the block chain; on the other hand, the evaluation content of the user is analyzed, abnormal consumption comments and abnormal users are marked, the good comment of the bill brushing and the bill brushing user can be identified, and further valuable reference is provided for the consumer.
Based on the same technical concept, the embodiment of the present invention correspondingly provides a computer device, as shown in fig. 5, the computer device 5 includes a memory 51 and a processor 52, the memory 51 stores a computer program, and when the processor 52 runs the computer program stored in the memory 51, the processor 52 executes the foregoing user evaluation management method.
Based on the same technical concept, embodiments of the present invention correspondingly provide a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the processor executes the user evaluation management method.
In summary, the user evaluation management method, system, computer device, and storage medium provided in the embodiments of the present invention apply a blockchain technology to user evaluation supervision of an internet transaction platform, and the blockchain technology brings new development to the review industry by the characteristics of transparent information disclosure, non-falsification, traceability, and the like, but the scheme adopted in the industry at present is to upload all evaluation information to a blockchain for storage, which results in a large scale of a user evaluation system, and the problem that all user review contents are linked up easily, which results in low data access efficiency, and occupies a storage space of the blockchain.
The block chain-based user evaluation management scheme adopts a combination mode of on-chain storage and off-chain storage, can effectively prevent the problem of large data storage scale of a block chain network, specifically, hash coding is carried out on user comment content to generate a comment content abstract, then only the comment content abstract and a comment content local storage address are stored on a chain, the abstract updating operation is carried out on additional comments, and analysis and storage of the user comment content are carried out under the chain (local database), so that the data reading efficiency can be improved, the transaction timeliness of the chain is improved, and the transaction delay is reduced.
In addition, the method also needs to analyze and process the user comment content before the user comment content abstract is linked, adopts a machine learning technology, realizes marking abnormal consumption comments such as a bill by text/picture similarity analysis, marks good marketing comments from the abnormal consumption comments, marks the users who send the abnormal consumption comments, provides correct and reliable consumption evaluation data for the consumers, and further measures the commodity value and makes correct consumption decisions through the marked evaluations and the users.
It will be understood by those of ordinary skill in the art that all or some of the steps of the methods, systems, functional modules/units in the devices disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. In a hardware implementation, the division between functional modules/units mentioned in the above description does not necessarily correspond to the division of physical components; for example, one physical component may have multiple functions, or one function or step may be performed by several physical components in cooperation. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as is well known to those of ordinary skill in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, Digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by a computer. In addition, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media as known to those skilled in the art.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A user evaluation management method is characterized by comprising the following steps:
storing the user comment content into a local database and acquiring a local storage address of the user comment content;
carrying out Hash coding on the comment content to obtain a comment content abstract, and uploading the comment content abstract and a comment content local storage address to a block chain;
extracting a summary of the comment content to be checked from the block chain, obtaining a local storage address of the comment content to be checked, and reading the comment content from a local database according to the local storage address of the comment content to be checked;
and performing Hash coding on the comment content read from the local database, comparing the coding result with the to-be-checked comment content abstract extracted from the block chain, and if the coding result is consistent with the to-be-checked comment content abstract, judging that the to-be-checked comment content is not tampered.
2. The method of claim 1, further comprising:
when a user adds comments to the same consumption, storing the added comment contents into a local database according to the local storage address of the original comment contents;
and carrying out hash coding on the original comment content and the additional comment content together to obtain an updated comment content abstract, and updating in the block chain.
3. The method of claim 1, wherein obtaining the local storage address of the comment content to be checked from the blockchain comprises:
calling a block chain gateway interface;
calling an intelligent contract query method;
and inquiring the summary of the comment content to be checked based on an intelligent contract inquiry method in the block chain to obtain a local storage address of the comment content to be checked.
4. The method of any of claims 1-3, further comprising, prior to storing the user comment content in the local database:
after the user carries out consumption evaluation, judging whether the user is abnormal consumption comment according to the current comment content of the user;
if so, marking the current comment of the user as an abnormal consumption comment, marking the user as an abnormal user, storing the current comment content of the user and the mark of the current comment content into a local database, and acquiring a local storage address of the local database.
5. The method according to claim 4, wherein the judging whether the comment is an abnormal consumption comment according to the current comment content of the user specifically includes:
and performing similarity analysis on the current comment content of the user based on the historical comment content of the current commodity and the historical comment content of the commodity with the same type and the same price, and judging whether the comment content is an abnormal consumption comment or not according to an analysis result.
6. The method of claim 5, wherein performing similarity analysis on the current comment content of the user based on the historical comment content of the current commodity and the historical comment content of the same-type commodity, and judging whether the current comment content is an abnormal consumption comment according to the analysis result, comprises:
acquiring current comment content of a user;
judging whether text comments exist in the current comment content of the user, if so, carrying out feature vector coding on the text comments, the historical comment content of the current commodity and the text information in the historical comment content of the commodity with the same type and the same price to obtain respective corresponding text feature vectors;
calculating first cosine similarity of text feature vectors of current comment contents of users and text feature vectors of historical comment contents of current commodities and historical comment contents of commodities with the same type and the same price;
and judging whether the first cosine similarity is larger than a first preset threshold value, and if so, considering that the current comment of the user is an abnormal consumption comment.
7. The method of claim 6, after obtaining the current comment content of the user, further comprising:
judging whether a picture comment exists in the current comment content of the user, if so, carrying out feature vector coding on the picture comment, the historical comment content of the current commodity and the picture information in the historical comment content of the same-type commodity to obtain respective corresponding image feature vectors;
calculating second cosine similarity of the image feature vector of the current comment content of the user and the image feature vectors of the historical comment content of the current commodity and the historical comment content of the commodity with the same type and the same price;
and judging whether the second cosine similarity is larger than a second preset threshold, and if so, considering that the current comment of the user is an abnormal consumption comment.
8. A user evaluation management system, comprising:
the storage module is used for storing the user comment content into a local database and acquiring a local storage address of the user comment content;
the encoding module is used for carrying out Hash encoding on the comment content to obtain a comment content abstract;
the uploading module is used for uploading the comment content abstract and the comment content local storage address to the block chain;
the extraction module is used for extracting the summary of the comment content to be checked from the block chain, obtaining the local storage address of the comment content to be checked, and reading the comment content from the local database according to the local storage address of the comment content to be checked;
the encoding module is also configured to perform hash encoding on the comment content read from the local database; and the number of the first and second groups,
and the comparison module is used for comparing the encoding result of the comment content read from the local database with the to-be-checked comment content abstract extracted from the block chain, and if the encoding result of the comment content read from the local database is consistent with the to-be-checked comment content abstract, judging that the to-be-checked comment content is not tampered.
9. A computer device characterized by comprising a memory in which a computer program is stored and a processor that executes the user evaluation management method according to any one of claims 1 to 7 when the processor runs the computer program stored in the memory.
10. A computer-readable storage medium on which a computer program is stored, wherein the computer program, when executed by a processor, causes the processor to execute the user rating management method according to any one of claims 1 to 7.
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