CN111090700B - Data management method and device and electronic equipment - Google Patents
Data management method and device and electronic equipment Download PDFInfo
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- CN111090700B CN111090700B CN201911297528.9A CN201911297528A CN111090700B CN 111090700 B CN111090700 B CN 111090700B CN 201911297528 A CN201911297528 A CN 201911297528A CN 111090700 B CN111090700 B CN 111090700B
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/27—Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/22—Indexing; Data structures therefor; Storage structures
- G06F16/2291—User-Defined Types; Storage management thereof
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
- G06F21/64—Protecting data integrity, e.g. using checksums, certificates or signatures
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- 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
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- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
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Abstract
The invention provides a data management method, a device and electronic equipment, wherein the method comprises the following steps: receiving a complaint request from a first user; the complaint request carries a data identifier of the complaint data of the second user and the user data of the first user; determining a first correlation degree between the first user and the complained data according to the user data of the first user; acquiring user data of a second user, and determining a second correlation degree between the second user and the complaint data based on the user data of the second user; and determining that the complained data belongs to the first user or the second user according to the first correlation degree and the second correlation degree. In the invention, when the user finds that the data belonging to the user is claimed by other users on the blockchain, the complaint request can be sent to calculate the correlation degree between the two users and the complaint data respectively, so as to determine the user to which the complaint data belongs.
Description
Technical Field
The present invention relates to the field of blockchain technologies, and in particular, to a data management method, a device, and an electronic device.
Background
With the rapid expansion of internet technology, the number of netizens continues to increase, and digital works such as self-media articles, photos, short videos, etc. continue to increase. The internet technology brings convenience to people and also brings inconvenience, for example, the behavior of infringing digital works occurs frequently due to low cost and low threshold of uploading data on the network.
In the related art, digital assets are managed by means of a blockchain technology, the blockchain technology is designed to enable the blockcontents to have the characteristic of being difficult to tamper, effective transactions recorded by the blockchain can be permanently checked, the digital assets are beneficial to users to claim, the claims can be recorded on the blockchain and cannot be changed, in the mode, the users cannot right the digital assets which have been claimed by other users on the blockchain and infringe the digital assets, and therefore the user experience is reduced.
Disclosure of Invention
The invention aims to provide a data management method, a data management device and electronic equipment, so that a user can conduct right maintenance on digital assets infringing own works, and user experience is improved.
In a first aspect, an embodiment of the present invention provides a data management method, including: receiving a complaint request from a first user; the complaint request carries a data identifier of the complaint data of the second user and the user data of the first user; determining a first correlation degree between the first user and the complained data according to the user data of the first user; acquiring user data of a second user, and determining a second correlation degree between the second user and the complaint data based on the user data of the second user; and determining that the complained data belongs to the first user or the second user according to the first correlation degree and the second correlation degree.
In an optional embodiment, after the step of determining that the complaint data belongs to the first user or the second user according to the first correlation degree and the second correlation degree, the method further includes: the first correlation, the second phase Guan Du and the user to whom the complaint data belong are saved to a preset blockchain.
In an alternative embodiment, before the step of determining the first relevance between the first user and the complained data according to the user data of the first user, the method further includes: calculating the association degree of the user data of the first user and the complaint data; if the association degree is greater than or equal to a preset threshold value, executing the first association degree between the first user and the complaint data according to the user data of the first user; if the association degree is smaller than a preset threshold value, returning the reject instruction to the client corresponding to the first user; wherein the reject instruction contains a reject cause.
In an alternative embodiment, the user data of the first user includes first proof data and first user information; the step of determining the first correlation between the first user and the complaint data according to the user data of the first user includes: determining a first data similarity according to the first proving data and the complaint data; determining a degree of association of the first user with the complaint data and a credit score of the first user based on the first user information and the complaint data; and carrying out weighted summation on the similarity of the first data, the association degree of the first user and the complained data and the credit score to obtain the first association degree of the first user and the complained data.
In an alternative embodiment, the step of determining the similarity of the first data according to the first proof data and the complaint data includes: and calculating the similarity of the complained data and the first proof data through a preset semantic analysis algorithm to obtain the first data similarity.
In an optional embodiment, the step of determining the association degree between the first user and the complaint data based on the first user information and the complaint data includes: inquiring historical uploading data corresponding to a first user based on the first user information; performing natural language processing on the historical uploading data to generate a text abstract corresponding to the first user; generating a abstract label according to the text abstract; the abstract label is a class of the historical data corresponding to the first user; and determining the association degree of the first user and the complaint data based on the abstract label corresponding to the first user and the abstract label corresponding to the complaint data.
In an alternative embodiment, the credit score of the first user is obtained by: inquiring historical behavior data and information perfection degree corresponding to a first user based on the first user information; analyzing the historical behavior data to obtain a credit value corresponding to the first user; the credit value and the information perfection degree are weighted and summed to determine the credit score of the first user.
In an optional embodiment, the step of obtaining the user data of the second user and determining the second correlation degree between the second user and the complaint data based on the user data of the second user includes: acquiring user data of a second user based on the complaint data; wherein the user data of the second user includes second attestation data and second user information; determining second data similarity according to the second proving data and the complaint data; determining a degree of association of the second user with the complaint data and a credit score of the second user based on the second user information and the complaint data; and carrying out weighted summation on the similarity of the second data, the association degree of the second user and the complained data and the credit score of the second user to obtain the second association degree of the second user and the complained data.
In a second aspect, an embodiment of the present invention provides a data management apparatus, including: the instruction receiving module is used for receiving a complaint request from a first user; the complaint request carries a data identifier of the complaint data of the second user and the user data of the first user; the first correlation determining module is used for determining a first correlation between the first user and complaint data according to the user data of the first user; the second correlation degree determining module is used for acquiring user data of a second user and determining the second correlation degree of the second user and the complaint data based on the user data of the second user; and the user determining module is used for determining that the complained data belongs to the first user or the second user according to the first correlation degree and the second correlation degree.
In a third aspect, an embodiment of the present invention provides an electronic device, including a processor and a memory storing machine-executable instructions executable by the processor to implement the data management method of any of the foregoing embodiments.
The embodiment of the invention has the following beneficial effects:
the invention provides a data management method, a data management device and electronic equipment, wherein a complaint request from a first user is received; the complaint request carries a data identifier of the complaint data of the second user and the user data of the first user; determining a first correlation degree between the first user and the complained data according to the user data of the first user; then obtaining user data of a second user, and determining a second correlation degree between the second user and the complaint data based on the user data of the second user; and determining that the complained data belongs to the first user or the second user according to the first correlation degree and the second correlation degree. In the invention, when the user finds that the data belonging to the user is claimed by other users on the blockchain, the complaint request can be sent to calculate the correlation degree between the two users and the complaint data respectively, so as to determine the user to which the complaint data belongs.
Additional features and advantages of the invention will be set forth in the description which follows, or in part will be obvious from the description, or may be learned by practice of the invention.
In order to make the above objects, features and advantages of the present invention more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a data management method according to an embodiment of the present invention;
FIG. 2 is a flowchart of another data management method according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a data management device according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Blockchain technology is a technology that has emerged in recent years. Blockchains are essentially a de-centralized database, which is a distributed ledger that is a serially connected transaction record cryptographically concatenated and protected against content. The design of blockchain technology makes blockcontents have the characteristic of difficult tampering, and effective transactions recorded by the blockchain can be permanently checked, so that a third party platform for claiming and managing digital works on the internet by means of the blockchain technology also appears, and a user can claiming the digital works on the internet through the platform, wherein the claiming can be recorded on the blockchain and cannot be changed.
With the advent of digital asset management platforms based on blockchain technology, users still face some infringement problems, such as that a work originally created by themselves is first logged on by others without knowledge, and claimed on a blockchain, but users cannot maintain the right of infringement of the data asset of the work originally created by themselves, resulting in poor user experience.
Based on the data management method and device and electronic equipment are provided in the embodiments of the present invention. The technique can be applied in the scenario of the right-to-hold complaints of various data on the blockchain. For the sake of understanding the present embodiment, first, a data management method disclosed in the present embodiment of the present invention is shown in fig. 1, and the method includes the following specific steps:
step S102, receiving a complaint request from a first user; the complaint request carries a data identifier of the complaint data of the second user and the user data of the first user.
The complaint request may be sent by the first user through a client, and the client may be understood as an application program installed on a mobile terminal (such as a mobile phone, a tablet computer, etc.) or a computer of the user, or may be a website accessed through a browser on the mobile terminal or the computer.
The first user may be a user who complains that the complained data currently belongs to a second user, and the second user is a complained user, and may also understand that the first user believes that the complained data which the second user claims is stolen or steals data which infringes the first user. The complaint data may be an article, a picture, a video, etc., and each complaint data corresponds to a data identifier, and the data identifier may be a number, a letter, a character string, etc., and may uniquely identify the data, through which the corresponding complaint data and the user to whom the complaint data currently belongs may be determined.
Step S104, according to the user data of the first user, determining a first correlation degree between the first user and the complaint data.
The user data of the first user may include detailed complaint materials uploaded by the first user through the client, and user information of the first user. In specific implementation, the complaint material is compared with the complaint data, and then the user information is used for inquiring the user history uploading data and comparing with the complaint data, so that the first correlation degree of the first user and the complaint data can be obtained, the first correlation degree can be a specific numerical value, the magnitude of the numerical value can represent the correlation degree of the first user and the complaint data, and the larger the numerical value is, the higher the correlation degree is.
Step S106, obtaining the user data of the second user, and determining a second correlation degree between the second user and the complaint data based on the user data of the second user.
The user data of the second user may be user history uploaded data searched on the blockchain, or may be supplementary material for proving that the data to be complained belongs to the user based on the data submitted by the user when the data to be complained is submitted before. And comparing the user history with the data submitted when the data is submitted, the supplementary material and the data to be complained, and obtaining a second correlation degree between the second user and the data to be complained, wherein the second correlation degree can be a specific numerical value, the magnitude of the numerical value can represent the correlation degree between the second user and the data to be complained, and the larger the numerical value is, the higher the correlation degree is.
Step S108, according to the first correlation degree and the second correlation degree, the complaint data is determined to belong to the first user or the second user.
The magnitude of the values of the first correlation and the second correlation may be compared to determine that the complaint data belongs to a user with a large scoring result. For example, when the first correlation is greater than the second correlation, it will be determined that the declared data belongs to the first user, and the determination process and the determination result are uploaded to the blockchain to modify or maintain the user to whom the declared data belongs.
The invention provides a data management method, firstly receiving a complaint request from a first user; the complaint request carries a data identifier of the complaint data of the second user and the user data of the first user; determining a first correlation degree between the first user and the complained data according to the user data of the first user; then obtaining user data of a second user, and determining a second correlation degree between the second user and the complaint data based on the user data of the second user; and determining that the complained data belongs to the first user or the second user according to the first correlation degree and the second correlation degree. In the invention, when the user finds that the data belonging to the user is claimed by other users on the blockchain, the complaint request can be sent to calculate the correlation degree between the two users and the complaint data respectively, so as to determine the user to which the complaint data belongs.
The embodiment of the invention also provides another data management method, which is realized on the basis of the method of the embodiment; the method focuses on describing a specific process (realized by the following steps S210-S214) of determining a first relativity of a first user and complained data according to user data of the first user; as shown in fig. 2, the method comprises the steps of:
step S202, receiving a complaint request from a first user; the complaint request carries a data identifier of the complaint data of the second user and the user data of the first user; wherein the user data of the first user includes first attestation data and first user information.
When the first user finds that the self rights are infringed, a complaint request can be sent through the client, wherein the complaint request carries a data identifier of complaint data currently belonging to the second user and user data of the first user, the user data comprises first user information and first proof data submitted by the first user, and the first proof material is complaint material for proving that the complaint data belongs to the first user; evidence of self-standing or a description of more accurate information in the data being complained may be included in the complaint material. The first user information generally includes registration information, real-name authentication information, personal information, and information integrity degree of the user.
Step S204, calculating the association degree between the user data of the first user and the complaint data.
In order to prevent users from wasting resources by malicious complaints or to guide users to provide complaint materials that are more complete and have a higher degree of association with the complaint data, it is necessary to initially calculate the degree of association of the user data of the first user with the complaint data. In specific implementation, keywords corresponding to the user data of the first user and keywords corresponding to the complaint data can be extracted respectively, and the similarity of the keywords and the complaint data is compared, namely, the association degree of the user data and the complaint data is obtained, and the complaint of the user is accepted or refused according to the association degree.
Step S206, judging whether the association degree of the user data of the first user and the complaint data is larger than or equal to a preset threshold value; if so, executing step S208; if greater than or equal to, step S210 is performed.
The above-mentioned preset threshold is usually set by the developer according to the requirement, and the higher the preset threshold is, the higher the requirement for submitted user data is.
Step S208, returning the reject instruction to the client corresponding to the first user; wherein the reject instruction comprises reject reasons; and (5) ending.
If the association degree is smaller than a preset threshold, the complaint request sent by the first user is refused, that is, the platform returns an refusing instruction to the client corresponding to the first user, and the refusing execution includes a refusing reason, which is generally the reason for determining that the association degree of the user data and the refused data is low. When the complaint request is rejected, the first user is required to rearrange the complaint material (corresponding to the user-pressed data) according to the reason of rejection.
If the association degree is greater than or equal to the preset threshold value, the complaint request is accepted, the platform freezes the complaint data on the block, namely, only allows other users to check the complaint data and does not allow other operations to be performed on the complaint data, meanwhile, the complaint data enters a arbitration flow to arbitrate the ownership of the complaint data, and the arbitration can be performed by an arbitration node of the platform.
Step S210, determining the first data similarity according to the first proving data and the complaint data of the first user.
In specific implementation, keywords corresponding to the first proving data of the first user and keywords corresponding to the complained data can be extracted respectively, and the first data similarity is determined according to the association degree of the keywords. The above step S210 may also be implemented by: and calculating the similarity of the complained data and the first proof data through a preset semantic analysis algorithm to obtain the first data similarity.
The above-described semantic analysis algorithm is a research method for revealing meaning of words and sentences by analyzing elements of language and syntactic contexts. The semantic analysis algorithm may employ doc2vec, etc. The language elements, the syntax and the like of the first proving data and the complained data can be analyzed through the semantic analysis algorithm to determine the data similarity of the first proving data and the complained data.
Step S212, determining a degree of association of the first user with the complaint data and a credit score of the first user based on the first user information and the complaint data.
The existing data under the user name and the basic attribute distribution of the existing data (which can also be understood as the belonging classification of the existing data) can be determined according to the first user information. And comparing the basic attribute distribution condition of the existing data determined by the first user information with the basic attribute of the complained data, and deducing the association degree of the first user and the complained data. In general, if data having the same attribute as the declared data under the user name, it is possible to determine that the degree of association of the first user with the declared data is 0.
The historical behavior data and the information perfection degree of the user can be determined according to the first user information; by analyzing the historical behavioral data of the user, a credit score for the first user may be determined.
The method for determining the association degree between the first user and the complained data in the step S212 may be implemented by the following steps 10-13:
step 10, inquiring historical uploading data corresponding to a first user based on the first user information; the historically uploaded data is the existing data under the user name.
Step 11, carrying out natural language processing on the historical uploading data to generate a text abstract corresponding to a first user; the natural language processing (N LP, natural Language Processing) is typically a technique of communicating with a computer using natural language, which may perform word segmentation, sentence analysis, semantic analysis, etc. with data.
Step 12, generating a abstract label according to the text abstract; the summary tag is a class to which the data corresponds to the first user, and may also be understood as a basic attribute distribution condition of the existing data under the name of the first user.
And step 13, determining the association degree of the first user and the complaint data based on the abstract label corresponding to the first user and the abstract label corresponding to the complaint data.
In a specific implementation, the account book information of the blockchain network can record the link address and the related summary label of the existing data, and has a public transparency which can not be modified. Therefore, the association degree between the first user and the complaint data can be determined according to the abstract label corresponding to the complaint data and the abstract label corresponding to the first user recorded on the blockchain.
In a specific implementation, the determining the credit score of the first user in the step S212 may be implemented by the following steps 20-22:
step 20, inquiring historical behavior data and information perfection degree corresponding to the first user based on the first user information; the historical behavior data comprises information such as whether the first user has issued unreal information or has impounded other data. And determining the information perfection degree according to the real-name authentication information and the personal information uploaded by the first user.
Step 21, analyzing the historical behavior data to obtain a credit value corresponding to the first user; typically, if the analysis results in the first user ever issuing infirm information or ever faking other data, the first user's credit value will be low.
And step 22, carrying out weighted summation on the credit value and the information perfection degree to determine the credit score of the first user. The weight of the credit value is generally greater than the weight of the information perfection degree.
Step S214, the first data similarity, the association degree of the first user and the complaint data and the credit score are weighted and summed to obtain the first association degree of the first user and the complaint data.
The similarity of the first data, the association degree of the first user with the data to be complained and the credit score are respectively corresponding to corresponding weights, and the weights generally have different values according to the importance degree, for example, the similarity of the first data, the association degree of the first user with the data to be complained and the credit score may be sequentially 5, 4 and 3. The first data similarity typically weighs more than the association of the first user with the complaint data, and the association of the first user with the complaint data weighs more than the credit score.
Step S216, acquiring user data of a second user based on the complaint data; wherein the user data of the second user includes second attestation data and second user information.
The second certification data may include the submitted data and supplementary materials of the complaint data submitted by the second user, and the second user information generally includes registration information, real-name authentication information, personal information, and perfect degree of information of the user.
Step S218, determining a second correlation degree between the second user and the complaint data according to the second proof data, the second user information and the complaint data.
In specific implementation, the step S218 may be implemented by the following steps 30-32:
step 30, determining second data similarity according to the second proving data and the complaint data;
step 31, determining the association degree of the second user and the complaint data and the credit score of the second user based on the second user information and the complaint data;
and step 32, weighting and summing the similarity of the second data, the association degree of the second user and the complaint data and the credit score of the second user to obtain the second association degree of the second user and the complaint data.
The method for determining the second phase Guan Du of the data to be complained and the second user is similar to the method for determining the first related data of the data to be complained and the first user, specifically, the similarity of the data to be complained and the second proof data can be calculated through a preset semantic analysis algorithm, so as to obtain the second data similarity.
The manner of determining the association degree between the second user and the complained data in the above step 31 may be: inquiring historical uploading data corresponding to the second user based on the second user information; the historical uploading data is the existing data under the user name; performing natural language processing on the historical uploading data to generate a text abstract corresponding to a second user; then generating a abstract label according to the text abstract; the abstract label is a category corresponding to the second user and belonging to the historical data; and determining the association degree of the second user and the complaint data based on the abstract label corresponding to the second user and the abstract label corresponding to the complaint data.
The manner of determining the credit score of the second user in the above step 32 may be: inquiring historical behavior data and information perfection degree corresponding to the second user based on the second user information; analyzing the historical behavior data to obtain a credit value corresponding to the second user; and then carrying out weighted summation on the credit value and the information perfection degree to determine the credit score of the first user.
Step S220, according to the first correlation degree and the second correlation degree, the complaint data is determined to belong to the first user or the second user.
In step S222, the first correlation, the second phase Guan Du and the user to whom the complaint data belong are saved to a predetermined blockchain.
And uploading the judging process and the judging result of the user to which the complained user belongs to a preset blockchain for storage, thereby being beneficial to recording the credit data of the user and facilitating the subsequent judgment of the user to which the complained data belongs.
According to the data management method, the first similarity and the second similarity can be calculated respectively according to the user data corresponding to the first user and the user data corresponding to the second user, the affiliated user is determined according to the similarity, and the similarity and the affiliated user are uploaded to the blockchain, so that the follow-up tracing of the data is facilitated.
Corresponding to the above data management method, the embodiment of the present invention further provides a data management device, as shown in fig. 3, where the device includes:
an instruction receiving module 30 for receiving a complaint request from a first user; the complaint request carries a data identifier of the complaint data of the second user and the user data of the first user.
The first relevance determining module 31 is configured to determine a first relevance between the first user and the complaint data according to the user data of the first user.
A second correlation determination module 32, configured to obtain user data of the second user, and determine a second correlation between the second user and the complaint data based on the user data of the second user.
The user determining module 33 is configured to determine that the complained data belongs to the first user or the second user according to the first correlation degree and the second correlation degree.
The data management device firstly receives a complaint request from a first user; the complaint request carries a data identifier of the complaint data of the second user and the user data of the first user; determining a first correlation degree between the first user and the complained data according to the user data of the first user; then obtaining user data of a second user, and determining a second correlation degree between the second user and the complaint data based on the user data of the second user; and determining that the complained data belongs to the first user or the second user according to the first correlation degree and the second correlation degree. When the user finds that the data belonging to the user is claimed by other users on the blockchain, a complaint request can be sent to calculate the correlation degree between the two users and the complaint data respectively, so that the user to which the complaint data belongs is determined.
Further, the apparatus further includes: a storage module for: the first correlation, the second phase Guan Du and the user to whom the complaint data belong are saved to a preset blockchain.
Further, the device further comprises an auditing module for: calculating the association degree of the user data of the first user and the complaint data; if the association degree is greater than or equal to a preset threshold value, executing the first association degree between the first user and the complained data according to the user data of the first user; if the association degree is smaller than the preset threshold value, returning the refusing instruction to the client corresponding to the first user; wherein the reject instruction contains a reject cause.
Specifically, the user data of the first user includes first certification data and first user information; the first correlation determination module 31 is configured to: determining a first data similarity according to the first proving data and the complaint data; determining a degree of association of the first user with the complaint data and a credit score of the first user based on the first user information and the complaint data; and carrying out weighted summation on the similarity of the first data, the association degree of the first user and the complained data and the credit score to obtain the first association degree of the first user and the complained data.
Further, the first correlation determination module 31 is further configured to: and calculating the similarity of the complained data and the first proof data through a preset semantic analysis algorithm to obtain the first data similarity.
Further, the first correlation determination module 31 is further configured to: inquiring historical uploading data corresponding to a first user based on the first user information; performing natural language processing on the historical uploading data to generate a text abstract corresponding to the first user; generating a abstract label according to the text abstract; the abstract label is a class of the historical data corresponding to the first user; and determining the association degree of the first user and the complaint data based on the abstract label corresponding to the first user and the abstract label corresponding to the complaint data.
Further, the first correlation determination module 31 is further configured to: inquiring historical behavior data and information perfection degree corresponding to a first user based on the first user information; analyzing the historical behavior data to obtain a credit value corresponding to the first user; the credit value and the information perfection degree are weighted and summed to determine the credit score of the first user.
Further, the second correlation determination module 32 is configured to: acquiring user data of a second user based on the complaint data; wherein the second user data includes second attestation data and second user information; determining second data similarity according to the second proving data and the complaint data; determining a degree of association of the second user with the complaint data and a credit score of the second user based on the second user information and the complaint data; and carrying out weighted summation on the similarity of the second data, the association degree of the second user and the complained data and the credit score of the second user to obtain the second association degree of the second user and the complained data.
The data management device provided in the embodiment of the present invention has the same implementation principle and technical effects as those of the foregoing method embodiment, and for brevity, reference may be made to the corresponding content in the foregoing method embodiment where the device embodiment is not mentioned.
The embodiment of the present invention further provides an electronic device, referring to fig. 4, where the electronic device includes a processor 101 and a memory 100, where the memory 100 stores machine executable instructions that can be executed by the processor 101, and the processor 101 executes the machine executable instructions to implement the data management method described above.
Further, the electronic device shown in fig. 4 further includes a bus 102 and a communication interface 103, and the processor 101, the communication interface 103, and the memory 100 are connected through the bus 102.
The memory 100 may include a high-speed random access memory (RAM, random Access Memory), and may further include a non-volatile memory (non-volatile memory), such as at least one magnetic disk memory. The communication connection between the system network element and at least one other network element is implemented via at least one communication interface 103 (which may be wired or wireless), and may use the internet, a wide area network, a local network, a metropolitan area network, etc. Bus 102 may be an ISA bus, a PCI bus, an EISA bus, or the like. The buses may be classified as address buses, data buses, control buses, etc. For ease of illustration, only one bi-directional arrow is shown in FIG. 4, but not only one bus or type of bus.
The processor 101 may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in the processor 101 or instructions in the form of software. The processor 101 may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU for short), a network processor (Network Processor, NP for short), etc.; but also digital signal processors (Digital Signal Processor, DSP for short), application specific integrated circuits (Application Specific Integrated Circuit, ASIC for short), field-programmable gate arrays (Field-Programmable Gate Array, FPGA for short) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The disclosed methods, steps, and logic blocks in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be embodied directly in the execution of a hardware decoding processor, or in the execution of a combination of hardware and software modules in a decoding processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in the memory 100 and the processor 101 reads information in the memory 100 and in combination with its hardware performs the steps of the method of the previous embodiments.
The embodiment of the invention also provides a machine-readable storage medium, which stores machine-executable instructions that, when being called and executed by a processor, cause the processor to implement the data management method, and the specific implementation can be referred to the method embodiment and will not be described herein.
It will be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process of the apparatus and/or the electronic device described above may refer to the corresponding process in the foregoing method embodiment, which is not described in detail herein.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.
Claims (9)
1. A method of data management, the method comprising:
receiving a complaint request from a first user; the complaint request carries a data identifier of the complaint data of the second user and the user data of the first user;
determining a first correlation degree between the first user and the complained data according to the user data of the first user;
acquiring user data of the second user, and determining a second correlation degree between the second user and the complaint data based on the user data of the second user;
determining that the complained data belongs to the first user or the second user according to the first correlation degree and the second correlation degree;
the user data of the first user comprises first proving data and first user information;
the step of determining a first relevance between the first user and the complaint data according to the user data of the first user comprises the following steps:
determining a first data similarity according to the first proving data and the complaint data;
determining a degree of association of the first user with the complained data and a credit score of the first user based on the first user information and the complained data;
and carrying out weighted summation on the similarity of the first data, the association degree of the first user and the complained data and the credit score to obtain the first association degree of the first user and the complained data.
2. The method of claim 1, wherein after the step of determining that the complaint data belongs to the first user or the second user based on the first correlation and the second correlation, the method further comprises:
and storing the first correlation degree, the second correlation degree and the user to which the complained data belong to a preset block chain.
3. The method of claim 1, wherein prior to the step of determining a first relevance of the first user to the complaint data based on user data of the first user, the method further comprises:
calculating the association degree of the user data of the first user and the complaint data;
if the association degree is greater than or equal to a preset threshold value, executing the first association degree between the first user and the complaint data according to the user data of the first user;
if the association degree is smaller than the preset threshold value, returning a rejection instruction to the client corresponding to the first user; wherein the reject instruction contains a reject cause.
4. The method of claim 1, wherein the step of determining a first data similarity from the first certification data and the complaint data comprises:
and calculating the similarity of the complained data and the first proving data through a preset semantic analysis algorithm to obtain the first data similarity.
5. The method of claim 1, wherein the step of determining a degree of association of the first user with the complaint data based on the first user information and the complaint data comprises:
inquiring historical uploading data corresponding to the first user based on the first user information;
performing natural language processing on the historical uploading data to generate a text abstract corresponding to the first user;
generating a abstract label according to the text abstract; the summary tag is a category to which the historical above data corresponding to the first user belongs;
and determining the association degree of the first user and the complaint data based on the abstract label corresponding to the first user and the abstract label corresponding to the complaint data.
6. The method of claim 1, wherein the credit score of the first user is obtained by:
inquiring historical behavior data and information perfection degree corresponding to the first user based on the first user information;
analyzing the historical behavior data to obtain a credit value corresponding to the first user;
and carrying out weighted summation on the credit value and the information perfection degree to determine the credit score of the first user.
7. The method of claim 1, wherein the step of obtaining user data for the second user, and determining a second relevance of the second user to the complaint data based on the user data for the second user, comprises:
acquiring user data of the second user based on the complaint data; wherein the user data of the second user includes second attestation data and second user information;
determining a second data similarity according to the second proof data and the complaint data;
determining a degree of association of the second user with the complained data and a credit score of the second user based on the second user information and the complained data;
and carrying out weighted summation on the second data similarity, the association degree of the second user and the complained data and the credit score of the second user to obtain the second association degree of the second user and the complained data.
8. A data management apparatus, the apparatus comprising:
the instruction receiving module is used for receiving a complaint request from a first user; the complaint request carries a data identifier of the complaint data of the second user and the user data of the first user;
the first relevance determining module is used for determining a first relevance between the first user and the complaint data according to the user data of the first user;
a second correlation determining module, configured to obtain user data of the second user, and determine a second correlation between the second user and the complaint data based on the user data of the second user;
the user determining module is used for determining that the complained data belongs to the first user or the second user according to the first correlation degree and the second correlation degree;
the user data of the first user comprises first proving data and first user information;
the first correlation determining module is further configured to determine a first data similarity according to the first proof data and the complaint data; determining a degree of association of the first user with the complained data and a credit score of the first user based on the first user information and the complained data; and carrying out weighted summation on the similarity of the first data, the association degree of the first user and the complained data and the credit score to obtain the first association degree of the first user and the complained data.
9. An electronic device comprising a processor and a memory, the memory storing machine executable instructions executable by the processor, the processor executing the machine executable instructions to implement the data management method of any of claims 1-7.
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