CN115470289A

CN115470289A - Risk data processing method, device and system

Info

Publication number: CN115470289A
Application number: CN202210907782.1A
Authority: CN
Inventors: 梁志勇
Original assignee: Jingdong Technology Information Technology Co Ltd
Current assignee: Jingdong Technology Information Technology Co Ltd
Priority date: 2022-07-29
Filing date: 2022-07-29
Publication date: 2022-12-13

Abstract

The invention discloses a risk data processing method, device and system, and relates to the technical field of computers. One embodiment of the method comprises: acquiring a plurality of pieces of risk data from a preset block chain and storing the risk data in the local; the risk data are written into the block chain in advance by a plurality of data providers, and any risk data provided by any data provider comprises a risk subject identifier and a risk level determined by the data provider for the risk subject identifier; and responding to a risk query request sent by a user side, determining risk data corresponding to a risk main body identifier carried in the risk query request in local data, and returning a query result including a risk grade in the determined risk data to the user side. The implementation mode can improve the availability of the risk data and the risk prevention capability of the enterprise.

Description

Risk data processing method, device and system

Technical Field

The invention relates to the technical field of computers, in particular to a risk data processing method, device and system.

Background

Currently, each internet enterprise generally performs risk portrayal on a mobile phone number and an IP address through an artificial intelligence technology such as machine learning, deep learning, graph calculation and the like, and finally forms risk data about the mobile phone number, the IP address and risk levels thereof, so as to prevent risk behaviors in registration, login, marketing, order form and payment links. However, because the risk data between enterprises cannot be shared and fused, and the risk level calculation mechanisms and logics of the enterprises are inconsistent, it is difficult for the enterprise end users who need the risk data to select and use the risk data, which is not favorable for risk prevention and business safety.

Disclosure of Invention

In view of this, embodiments of the present invention provide a risk data processing method, apparatus, and system, which can integrate and fuse risk data of multiple data providers and provide query services to users in a centralized manner, and store related data through a blockchain for tracing, so as to effectively improve the availability of the risk data and the risk prevention capability of enterprises.

To achieve the above object, according to one aspect of the present invention, a risk data processing method is provided.

The risk data processing method of the embodiment of the invention comprises the following steps: acquiring a plurality of pieces of risk data from a preset block chain and storing the risk data in the local; the risk data are written into the block chain in advance by a plurality of data providers, and any risk data provided by any data provider comprises a risk subject identifier and a risk level determined by the data provider for the risk subject identifier; and in response to receiving a risk query request sent by a user side, determining risk data corresponding to a risk main body identifier carried in the risk query request in local data, and returning a query result including a risk grade in the determined risk data to the user side.

Optionally, the acquiring multiple pieces of risk data from a preset block chain and storing the multiple pieces of risk data locally includes: for any acquired risk data provided by any data provider: when judging that risk data which are provided by the data provider and have the same risk subject identification with the risk data do not exist locally, storing any risk data locally; and when judging that the risk data with the same risk subject identification as the risk data locally exists, replacing the risk data with any risk data with the same risk subject identification as the risk data.

Optionally, any risk data further includes a risk subject identifier and an applicable scenario of a risk level, and the risk query request further carries the applicable scenario; and determining, in the local data, risk data corresponding to the risk subject identifier carried in the risk query request, including: and determining risk subject identification carried in the risk query request and risk data corresponding to the applicable scene in the local data.

Optionally, any risk data further includes a validity period of the risk data; and determining, in the local data, risk data corresponding to the risk subject identifier carried in the risk query request, and returning a query result including the risk level in the determined risk data to the user side, including: determining risk subject identification carried in the risk query request and risk data which correspond to an applicable scene and are in an effective state in local data, and taking the determined risk data as target risk data; and returning a query result comprising the risk level in the target risk data to the user side.

Optionally, the risk query request further carries a tolerance of false alarm; and returning the query result including the risk level in the target risk data to the user side, wherein the step of returning the query result including the risk level in the target risk data to the user side comprises the following steps: determining the risk level in the target risk data as the risk level in the query result under the condition that the number of the target risk data is one, or the number of the target risk data is multiple and multiple pieces of target risk data contain the same risk level; in the case where the number of the target risk data is plural and the plural pieces of target risk data contain different risk levels: if the false alarm tolerance represents high false alarm tolerance, determining the highest risk grade in the different risk grades as the risk grade in the query result; and if the false alarm tolerance represents low false alarm tolerance, determining the lowest risk grade in the different risk grades as the risk grade in the query result.

Optionally, the method further comprises: after the query result is returned to the user side, determining the value data of the user side in single query according to the pre-stored historical transaction record of the user side; determining the contribution degree of each data provider according to the number of the data providers providing the risk level in the query result; and performing a lubricating operation to each data provider based on the value data and the contribution degree of each data provider.

Optionally, the method further comprises: after the query result is returned to the user side, receiving feedback information aiming at the query result sent by the user side; determining value data of single query of the user side according to a pre-stored historical transaction record of the user side; determining the contribution degree of each data provider according to the number of the data providers providing the risk level in the query result and the feedback information; and executing the moistening operation to each data provider based on the value data and the contribution degree of each data provider.

Optionally, the historical transaction record is written into the block chain in advance, and the historical transaction record includes: user identification, transaction amount and total number of queries; and, the method further comprises: after the query result is returned to the user side, generating a corresponding query record and writing the query record into the block chain for the query of the data provider; the query record comprises: the user identification, the risk subject identification carried in the risk query request, the query result, the identification of a data provider providing the risk level in the query result and the query time; and after the feedback information is received, generating a corresponding feedback record according to the feedback information and writing the feedback record into the block chain for the query of the data provider.

Optionally, the method further comprises: after determining the contribution degree of each data provider, generating contribution degree data comprising the identifier of the data provider providing the risk level in the query result, the contribution degree and the corresponding query record identifier, and writing the contribution degree data into the blockchain for the query of the data provider; after the partial wetting operation is executed, a partial wetting record comprising a partial wetting result and the corresponding contribution data identification is generated and written into the block chain for the data provider to inquire.

Optionally, the blockchain is maintained by the multiple data providers, the risk data further includes a data source and a risk tag characterizing a risk type, the query result further includes the risk tag, and the risk subject identifier includes a mobile terminal number and/or an IP address; the acquiring a plurality of risk data from a preset block chain and storing the risk data locally comprises: receiving a plurality of pieces of risk data pushed by the blockchain through execution of a pre-deployed intelligent contract; the intelligent contract is used for pushing when the block chain has newly added or updated risk data; the method further comprises the following steps: and in response to the received risk query request, when the target risk data does not exist in the local data, returning information without a matching result to the user side.

To achieve the above object, according to another aspect of the present invention, there is provided a risk data processing apparatus.

The risk data processing device of the embodiment of the invention can comprise: the data synchronization unit is used for acquiring a plurality of pieces of risk data from a preset block chain and storing the risk data in local; the risk data are written into the block chain in advance by a plurality of data providers, and any risk data provided by any data provider comprises a risk subject identifier and a risk level determined by the data provider for the risk subject identifier; an external service unit for: and responding to a risk query request sent by a user side, determining risk data corresponding to a risk main body identifier carried in the risk query request in local data, and returning a query result including a risk grade in the determined risk data to the user side.

To achieve the above object, according to another aspect of the present invention, there is provided a risk data processing system.

The risk data processing system of the embodiment of the invention can comprise: a plurality of data providers, a blockchain maintained by the plurality of data providers, and a risk identification system; the risk identification system acquires a plurality of pieces of risk data from a preset block chain and stores the risk data locally; the risk data are written into the block chain in advance by the multiple data providers, and any risk data provided by any data provider comprises a risk subject identifier and a risk level determined by the data provider for the risk subject identifier; in response to receiving a risk query request sent by a user side, the risk identification system determines risk data corresponding to a risk main body identifier carried in the risk query request in local data, and returns a query result including a risk level in the determined risk data to the user side.

Optionally, after returning the query result to the user side, the risk identification system receives feedback information for the query result sent by the user side, determines value data of a single query of the user side according to a pre-stored historical transaction record of the user side, determines contribution degrees of the data providers according to the number of data providers providing risk levels in the query result and the feedback information, and performs a sub-lubrication operation on the data providers based on the value data and the contribution degrees of the data providers; wherein the historical transaction record is written into the block chain by the risk identification system in advance, and the historical transaction record comprises: user identification, transaction amount and total number of queries; after the query result is returned to the user side, the risk identification system generates a corresponding query record and writes the query record into the block chain so as to be queried by the data provider; the query record comprises: the user identification, the risk subject identification carried in the risk query request, the query result, the identification of a data provider providing the risk level in the query result and the query time; after receiving the feedback information, the risk identification system generates a corresponding feedback record according to the feedback information and writes the feedback record into the block chain for the query of the data provider; after determining the contribution degree of each data provider, the risk identification system generates contribution degree data including an identifier of the data provider providing the risk level in the query result, the contribution degree and a corresponding query record identifier, and writes the contribution degree data into the blockchain for the data provider to query; after the dividing and lubricating operation is executed, the risk identification system generates a dividing and lubricating record comprising a dividing and lubricating result and the corresponding contribution degree data identification and writes the dividing and lubricating record into the block chain for the data provider to inquire.

To achieve the above object, according to still another aspect of the present invention, there is provided an electronic apparatus.

An electronic device of the present invention includes: one or more processors; and the storage device is used for storing one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors realize the risk data processing method provided by the invention.

To achieve the above object, according to still another aspect of the present invention, there is provided a computer-readable storage medium.

A computer-readable storage medium of the present invention has stored thereon a computer program which, when executed by a processor, implements the risk data processing method provided by the present invention.

According to the technical scheme of the invention, the embodiment of the invention has the following advantages or beneficial effects:

after the risk data are linked by a plurality of data providers, the block chain automatically synchronizes the newly added or updated risk data to the risk identification system by executing an intelligent contract, then the risk identification system can provide a risk data query service for a user through an interface, after a user side initiates a risk query request aiming at a certain mobile phone number or IP address to the risk identification system, the risk identification system determines corresponding risk data in the valid period from local data, and integrates the risk data into a query result to return to the user side, thereby realizing the risk data sharing and fusion of each data provider, improving the availability of the risk data and the risk prevention capability of enterprises, and ensuring the data security and traceability in the whole process through the block chain. In addition, after the risk identification system provides query service, the contribution degree of each data provider can be reasonably determined according to the historical transaction record, the query record and the feedback record of the user, the data providers are subjected to lubrication based on the contribution degree, and the historical transaction record, the query record and the feedback record, the contribution degree data and the lubrication record are written into a block chain, so that efficient and fair value lubrication and query and traceability of the related lubrication records are realized.

Further effects of the above-mentioned non-conventional alternatives will be described below in connection with the embodiments.

Drawings

The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:

FIG. 1 is a schematic diagram of the main steps of a risk data processing method according to an embodiment of the present invention;

FIG. 2 is a schematic diagram of an implementation architecture of a risk data processing method according to an embodiment of the present invention;

FIG. 3 is a schematic diagram of the components of a risk data processing apparatus according to an embodiment of the present invention;

FIG. 4 is a schematic diagram of the components of a risk data processing system in an embodiment of the invention;

FIG. 5 is an exemplary system architecture diagram in which embodiments of the present invention may be applied;

fig. 6 is a schematic structural diagram of an electronic device for implementing the risk data processing method in the embodiment of the present invention.

Detailed Description

Exemplary embodiments of the invention are described below with reference to the accompanying drawings, in which various details of embodiments of the invention are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

It should be noted that the embodiments of the present invention and the technical features of the embodiments may be combined with each other without conflict.

Fig. 1 is a schematic diagram of the main steps of a risk data processing method according to an embodiment of the present invention.

As shown in fig. 1, the risk data processing method according to the embodiment of the present invention may be executed by a risk identification system in the architecture shown in fig. 2, and includes the following specific steps:

step S101: and acquiring a plurality of pieces of risk data from a preset block chain and storing the risk data locally.

In embodiments of the present invention, risk data may be processed using a blockchain technique. The block chain is an integrated innovation application mode of computer technologies such as fused distributed storage, point-to-point data transmission, a consensus mechanism and an encryption algorithm. In general, a blockchain is an organization structure of information resources, and is logically a linked list formed by one or more blocks, where the blocks are used to store one or more information resources such as transaction records and intelligent contracts, and each node in a blockchain-based distributed network (hereinafter referred to as a blockchain network) holds the same blockchain, and the blockchain network is composed of the nodes holding blockchains.

The block chain can be divided into a public chain, a private chain and a alliance chain according to the application range and different node admission objects. Wherein: public chains are open to all people on the internet, such as bitcoin, etherhouse. Private chains are typically used inside organizations and are not open to others. Federation chains are open to specific organizations and enterprises. Some of the terms appearing in the examples of the present invention are explained below:

consensus: block chain terminology. There is no single accounting center on the blockchain, and the ledgers are recorded jointly by the whole network, which raises a problem of how to determine whether a transaction is legal. The process of determining whether a transaction is legitimate and whether a block of packets recommended by a node is approved by a public is called consensus, and only blocks that pass the consensus are approved by the whole network.

An intelligent contract: a piece of program code on the blockchain specifies the rights and obligations of the contractual associate and the terms of the action. Contract associates confirm smart contracts by sending blockchain transactions, paying for digital currency that the contracts may require, invoking functions specified by the smart contracts, and the like.

Referring to fig. 2, a blockchain network is formed by connecting a plurality of data providers (i.e., enterprise terminals that generate risk data) with each other, each data provider jointly maintains a blockchain as a node of the blockchain network, and each data provider can write the risk data that is formed by each data provider into a blockchain so as to implement reliable storage and non-tampering of the risk data.

The block chain can also synchronize newly-added or updated risk data to the risk identification system by executing a pre-deployed intelligent contract, so that the risk identification system provides a risk data query service to the outside according to the latest risk data stored locally, and fusion of multi-platform risk data and convenience of risk query are realized on the premise of meeting data security.

In specific application, any risk data may include a risk subject identifier and a risk level determined by a data provider for the risk subject identifier, and the risk subject identifier is used as a subject for risk judgment, and may include a mobile terminal number and an IP address, or may be other data with a specific similar function; the risk level may be expressed numerically or in words, for example, a zero score indicates no risk, a 1 score indicates very low risk, a 2 score indicates low risk, a 3 score indicates medium risk, a 4 score indicates medium risk, and a 5 score indicates high risk, and generally, the higher the risk level is, the more serious the risk is. Optionally, the risk data may further include applicable scenarios of risk subject identification and risk level, where the applicable scenarios are related to business, and may be registration, login, marketing, order, payment, and the like; the risk data may further include a validity period of the risk data, which may be in the form of a date or a remaining time length, and the validity period may be used to determine whether any of the risk data is currently in a valid state. In some embodiments, the risk data may further include a data source and a risk label characterizing a risk type, exemplarily, the data source may be self and external, respectively representing from inside and outside the data provider, and the risk label may be an abnormal registration for a mobile terminal number, an abnormal login, an abnormal payment, a malicious after-sale, and a general proxy IP for an IP address, a second dial proxy IP, a genuine cheat, a device masquerade, and the like. The risk data are predetermined by the data provider according to the calculation rule of the data provider and are written into the block chain.

In this step, the risk identification system automatically obtains a plurality of pieces of risk data from the blockchain and stores the risk data locally. In practical applications, after the block chain stores the newly added or updated risk data (the updating refers to the risk data added with the same risk subject identifier), the risk data is pushed to the risk identification system by executing the pre-deployed intelligent contract, so that the risk data is synchronized between the block chain and the risk identification system. For the risk identification system, for any acquired risk data (hereinafter referred to as first data) provided by any data provider: when judging that risk data (hereinafter referred to as associated data of the first data) which is provided by the data provider and has the same risk subject identification as the first data does not exist locally, storing the first data locally; and when judging that the associated data of the first data exists locally, replacing the associated data with the first data. That is, since stored data in the blockchain is not removable and modifiable, risk data of versions of the same data provider and the same risk subject identifier for a historical period is stored in the blockchain, but only risk data of the latest version of the same data provider and the same risk subject identifier may be stored in the risk identification system. In practical application, the factors of the applicable scenario can be further considered in the data synchronization process, that is, only one piece of risk data of the same data provider, the same risk subject identifier and the latest version of the same applicable scenario can be stored in the risk identification system.

Step S102: and in response to receiving a risk query request sent by a user side, determining risk data corresponding to a risk main body identifier carried in the risk query request in the local data, and returning a query result including the risk level in the determined risk data to the user side.

In this step, the risk identification system may provide a risk query service to a user side based on the locally stored risk data, where the user side may be an enterprise user or an individual user. In practical application, a user side sends a risk query request aiming at a certain risk main body identifier to a risk identification system for querying, the risk query request can also carry an application scene specified by a user and a false alarm tolerance provided by the user, the false alarm tolerance can indicate high false alarm tolerance (namely, the risk identification system can have a certain false alarm rate, and the high false alarm tolerance can be quantified by comparing with a preset false alarm rate threshold), and can also indicate low false alarm tolerance (namely, the false alarm rate of the risk identification system needs to be smaller than the false alarm rate threshold), the false alarm tolerance can have two options of 'high false alarm tolerance' and 'low false alarm tolerance', and can also be represented by the highest received false alarm rate.

After receiving the risk query request, the risk identification system may locally query risk data corresponding to the risk subject identifier in the risk query request, and form a query result according to the risk data and return the query result to the user side. If the risk query request carries the applicable scene, the risk identification system can locally query risk data corresponding to the risk subject identifier and the applicable scene in the risk query request, and forms a query result according to the risk data and returns the query result to the user side. As a preferred scheme, after receiving the risk query request, the risk identification system determines, in the local data, the risk subject identifier carried in the risk query request and the risk data corresponding to the applicable scenario and in an effective state, takes the determined risk data as target risk data, and returns a query result including the risk level in the target risk data to the user side. In a specific application, the above query result may include a risk label in addition to the risk level. It can be understood that if the risk identification system determines that there is no target risk data in the local data, information without matching result is returned to the user side.

Preferably, the risk level in the query result is determined by: determining the risk level in the target risk data as the risk level in the query result (referred to as a first integration strategy above) when the number of the target risk data is one, or the number of the target risk data is multiple and multiple pieces of target risk data contain the same risk level; in the case where the number of pieces of target risk data is plural and the plural pieces of target risk data contain different risk levels: if the false alarm tolerance in the risk query request represents high false alarm tolerance, determining the highest risk grade in different risk grades as the risk grade in the query result (referred to as a second integration strategy); if the false positive tolerance in the risk query request represents low false positive tolerance, the lowest risk level in the different risk levels is determined as the risk level in the query result (referred to as the third integration strategy above).

The principle of the above strategy is that for the second integration strategy, since the tolerance of the false alarm of the user is high, adverse effects of information security and risk subject identification on the service can be considered preferentially, and the high probability of false alarm which may exist in the high risk level is weakened or ignored, so that the highest risk level in the multiple risk levels is returned to the user. For the third consolidation strategy, the lowest risk level of the multiple risk levels is returned to the user, since the user does not allow a higher false alarm rate, while the false alarm probability corresponding to the high risk level is generally higher than the low risk level.

The risk labels in the query result can be combined by the risk labels contained in the target risk data where the risk grade is located in the query result, and then the query result can be formed based on the risk grade and the risk labels and returned to the user side. Of course, query results may be formed and returned to the user based solely on risk level. Therefore, risk data sharing and fusion of each data provider can be realized, the availability of the risk data and the risk prevention capability of enterprises are improved, and the data safety and traceability in the whole process are ensured through the block chain.

Thereafter, the relevant data providers can be differentiated for this query activity. Because the user generally obtains the risk query service provided by the risk identification system by purchasing a resource package and the like, each query of the user side corresponds to a corresponding value, and because the risk query service depends on the risk data of each data provider, a fair and reasonable distribution scheme needs to be realized by the risk identification system, the value of each query is distributed to the relevant data providers, and meanwhile, the maintenance of relevant data records in the distribution process is ensured, so that the data providers can query and check at any time. In the embodiment of the present invention, there may be the following two types of lubrication strategies:

the first moisturizing strategy considers only the query process. Specifically, after returning the query result to the user side, the risk identification system first determines the value data of a single query of the user side according to the pre-stored historical transaction record of the user side. Illustratively, the historical transaction record may be a record of the user purchasing the resource package, may include the user identification (which may be the PIN code of the purchased resource package), the transaction amount and the total number of queries, and may also include data such as the rules of the resource package, the number of resource packages, the purchase time, the expiration time, and the like. The historical transaction records are written into the block chain by the risk identification system after being generated so as to be inquired by the data provider. In the above steps, the transaction amount in the historical transaction record may be divided by the total number of queries to obtain the value of a single query.

Thereafter, the contribution of each data provider is determined based on the number of data providers that provide the risk level in the query result. The contribution degrees are used for representing importance degree weights of the data providers in the inquiry process, and the sum of the contribution degrees of the data providers can be 1 or a preset number between zero and 1. For example, when there is one data provider that forms the query result, the contribution degree may be counted as 1 (the case of not considering the wetting of the risk identification system itself, and the following case is mainly taken as an example), and when considering the wetting of the risk identification system itself, the contribution degree of the data provider may be counted as 0.9 (0.1 is the contribution degree of the risk identification system). When the number of data providers forming the query result is two, the contribution degrees of the two data providers can be respectively counted to be 0.5.

Finally, the risk identification system may perform a differentiated operation to each data provider based on the value data of the single query and the contribution of each data provider, where the value of each data provider differentiated may be the product of the value of the single query and its contribution. In practical application, the risk identification system can directly interact with a data provider to perform the moisture differentiation, and the risk identification system can also interact with the data provider through a statistical data gateway to perform the moisture differentiation.

The second moisturizing strategy considers the feedback of the user end to the query result besides the query process. Specifically, after returning the query result to the user side, the risk identification system may receive feedback information for the query result sent by the user side, where the feedback information may be positive feedback (non-misinformation) or negative feedback (misinformation). It can be understood that the user side receives the corresponding feedback after executing the corresponding action according to the risk level in the query result, and further generates the feedback information and sends the feedback information to the risk identification system. In the lubricating process, the risk identification system also determines the value data of the user side in single query according to the pre-stored historical transaction records of the user side, and then determines the contribution degree of each data provider according to the number of the data providers providing the risk level in the query result and the feedback information. Specifically, if the feedback information is positive feedback, calculating the contribution degree of the data provider according to a mode in a first distribution strategy; if the feedback information is negative feedback, for the first integration strategy and the third integration strategy, the contribution degree of the data provider is still calculated according to the mode in the first lubrication division strategy; and if the feedback information is negative feedback, reducing the contribution degree of each data provider according to a preset rule for the second integration strategy. Finally, the risk identification system performs a diff operation to each data provider based on the value data and the contribution of each data provider.

The principle of the above contribution degree adjustment is that the first integration strategy and the third integration strategy are already conservative strategies, and if false alarm still occurs, it indicates that data error occurs due to other reasons, so that the contribution degree and the diversity of the data provider are not reduced. For the second integration strategy, if the corresponding data provider is not differentiated after obtaining the false positive feedback, the data provider may have an incentive to increase the risk level for profit by itself, and therefore the generation of the incentive can be prevented by adjusting the appropriate contribution degree. For example, if a query process for executing the second integration policy at a certain time receives a false report fed back by the user side after a period of time, and the query process involves two data providers, the query process can be adjusted by 10% on the basis of the normal contribution degree of 0.5, and the final contribution degrees of the two data providers are respectively 0.45.

In particular, after returning the query result to the user side, the risk identification system may generate a corresponding query record and write the query record into the blockchain for the data provider to query. The query records include: the risk query method comprises the steps of user identification, risk main body identification carried in a risk query request, query results, identification of a data provider providing risk levels in the query results and query time. After receiving the feedback information, the risk identification system may generate a corresponding feedback record according to the feedback information and write the feedback record into the block chain for the data provider to query. In practical applications, the query record and the feedback record may be associated with each other through data such as a user identifier, and the two records may also be associated with the historical transaction record.

In addition, after determining the contribution degree of each data provider, the risk identification system may generate contribution degree data including an identification of the data provider providing the risk level in the query result, the contribution degree, and a corresponding query record identification, and write the contribution degree data into the blockchain for querying by the data provider. After performing the triage operation, the risk identification system may generate a triage record including the triage result and the corresponding contribution data identification and write the triage record into the blockchain for the data provider to query. The contribution data and the diff record can be correlated with a certain corresponding query, a historical transaction record, a query record and a feedback record, so that the relevant information and the diff basis of a query process are completely reflected. Therefore, efficient and fair value distribution and safe storage, inquiring and traceability of relevant records in risk inquiry and distribution processes can be realized.

In the technical scheme of the embodiment of the invention, after risk data are linked by a plurality of data providers, the block chain automatically synchronizes newly added or updated risk data to a risk identification system by executing an intelligent contract, then the risk identification system provides risk data query service for a user through an interface, and after a user terminal initiates a risk query request aiming at a certain mobile phone number or IP address to the risk identification system, the risk identification system determines corresponding risk data in the effective period from local data and integrates the risk data into a query result to be returned to the user terminal, so that the risk data sharing and fusion of each data provider are realized, the availability of the risk data and the risk prevention capability of enterprises are improved, and the data security and traceability in the whole process are ensured through the block chain. In addition, after the risk identification system provides query service, the contribution degree of each data provider can be reasonably determined according to the historical transaction record, the query record and the feedback record of the user, the data providers are subjected to lubrication based on the contribution degree, and the historical transaction record, the query record and the feedback record, the contribution degree data and the lubrication record are written into a block chain, so that efficient and fair value lubrication and query and traceability of the lubrication related records are realized.

It should be noted that, for the convenience of description, the foregoing method embodiments are described as a series of acts, but those skilled in the art will appreciate that the present invention is not limited by the order of acts described, and that some steps may in fact be performed in other orders or concurrently. Moreover, those skilled in the art will appreciate that the embodiments described in the specification are presently preferred and that no acts or modules are necessarily required to implement the invention.

To facilitate a better implementation of the above-described aspects of embodiments of the present invention, the following also provides related apparatus for implementing the above-described aspects.

Referring to fig. 3, a risk data processing apparatus 300 according to an embodiment of the present invention may include: a data synchronization unit 301 and an external service unit 302.

The data synchronization unit 301 may be configured to obtain multiple pieces of risk data from a preset block chain and store the multiple pieces of risk data locally; the risk data is written into the block chain in advance by a plurality of data providers, and any risk data provided by any data provider comprises a risk subject identifier and a risk level determined by the data provider for the risk subject identifier; the foreign service unit 302 may be configured to: and in response to receiving a risk query request sent by a user side, determining risk data corresponding to a risk main body identifier carried in the risk query request in local data, and returning a query result including a risk grade in the determined risk data to the user side.

In an embodiment of the present invention, the data synchronization unit 301 may further be configured to: for any acquired risk data provided by any data provider: when judging that risk data which are provided by the data provider and have the same risk subject identification with the risk data do not exist locally, storing any risk data locally; and when judging that the risk data with the same risk subject identification as the risk data locally exists, replacing the risk data with any risk data with the same risk subject identification as the risk data.

In a specific application, any risk data may further include a risk subject identifier and a risk level, and the risk query request further carries the applicable scenario; and, the outbound service unit 302 may be further configured to: and determining the risk subject identification carried in the risk query request and the risk data corresponding to the applicable scene in the local data.

In practice, any risk data may further include the validity period of the risk data; and, the outbound service unit 302 may be further configured to: determining risk subject identification carried in the risk query request and risk data which correspond to an applicable scene and are in an effective state in local data, and taking the determined risk data as target risk data; and returning the query result comprising the risk level in the target risk data to the user side.

As a preferred scheme, the risk query request further carries a false alarm tolerance; and, the outbound service unit 302 may be further configured to: determining the risk level in the target risk data as the risk level in the query result under the condition that the number of the target risk data is one, or the number of the target risk data is multiple and multiple pieces of target risk data contain the same risk level; in the case where the number of the target risk data is plural and the plural pieces of target risk data contain different risk levels: if the false alarm tolerance represents high false alarm tolerance, determining the highest risk grade in the different risk grades as the risk grade in the query result; and if the false alarm tolerance represents low false alarm tolerance, determining the lowest risk grade in the different risk grades as the risk grade in the query result.

Preferably, the device 300 may further comprise a moistening unit for: after the query result is returned to the user side, determining the value data of the user side in single query according to the pre-stored historical transaction record of the user side; determining contribution degrees of the data providers according to the number of the data providers providing the risk levels in the query result; and executing the moistening operation to each data provider based on the value data and the contribution degree of each data provider.

In one embodiment, the partial lubrication unit is further configured to: after the query result is returned to the user side, receiving feedback information aiming at the query result and sent by the user side; determining value data of single query of the user side according to a pre-stored historical transaction record of the user side; determining contribution degrees of the data providers according to the number of the data providers providing the risk levels in the query result and the feedback information; and performing a lubricating operation to each data provider based on the value data and the contribution degree of each data provider.

In an optional technical solution, the historical transaction record is written into the block chain in advance, and the historical transaction record includes: user identification, transaction amount and total number of queries; and, the apparatus 300 may further include an uplink unit for: after the query result is returned to the user side, generating a corresponding query record and writing the query record into the block chain for the data provider to query; the query record comprises: the user identification, the risk subject identification carried in the risk query request, the query result, the identification of a data provider providing the risk level in the query result and the query time; and after the feedback information is received, generating a corresponding feedback record according to the feedback information and writing the feedback record into the block chain for the query of the data provider.

In practical scenarios, the uplink unit may be further configured to: after determining the contribution degree of each data provider, generating contribution degree data comprising the identifier of the data provider providing the risk level in the query result, the contribution degree and the corresponding query record identifier, and writing the contribution degree data into the block chain for the data provider to query; after the partial wetting operation is executed, a partial wetting record comprising a partial wetting result and the corresponding contribution data identification is generated and written into the block chain for the data provider to inquire.

In addition, in the embodiment of the present invention, the blockchain is maintained by the plurality of data providers, the risk data further includes a data source and a risk tag representing a risk type, the query result further includes the risk tag, and the risk subject identifier includes a mobile terminal number and/or an IP address; the data synchronization unit 301 may be further configured to: receiving a plurality of pieces of risk data pushed by the blockchain through execution of a pre-deployed intelligent contract; the intelligent contract is used for pushing when the block chain has newly increased or updated risk data; the foreign service unit 302 may be further configured to: and in response to the received risk query request, when the target risk data does not exist in the local data, returning information without a matching result to the user side.

According to the technical scheme of the embodiment of the invention, after risk data are linked by a plurality of data providers, newly added or updated risk data are automatically synchronized to a risk identification system through executing an intelligent contract by a block chain, then the risk identification system can provide risk data query service for a user through an interface, after a user terminal initiates a risk query request aiming at a certain mobile phone number or IP address to the risk identification system, the risk identification system determines corresponding risk data in the effective period from local data, and further integrates the risk data into a query result to be returned to the user terminal, so that the risk data sharing and fusion of each data provider are realized, the availability of the risk data and the risk prevention capability of enterprises are improved, and the data security and traceability in the whole process are ensured through the block chain. In addition, after the risk identification system provides query service, the contribution degree of each data provider can be reasonably determined according to the historical transaction record, the query record and the feedback record of the user, the data providers are subjected to lubrication based on the contribution degree, and the historical transaction record, the query record and the feedback record, the contribution degree data and the lubrication record are written into a block chain, so that efficient and fair value lubrication and query and traceability of the related lubrication records are realized.

Fig. 4 is a schematic diagram of a component of a risk data processing system according to an embodiment of the present invention, and referring to fig. 4, the risk data processing system according to an embodiment of the present invention may include: a plurality of data providers, a blockchain maintained by the plurality of data providers, and a risk identification system. The risk identification system acquires a plurality of pieces of risk data from a preset block chain and stores the risk data in the local; the risk data are written into the block chain in advance by the multiple data providers, and any risk data provided by any data provider comprises a risk subject identifier and a risk level determined by the data provider for the risk subject identifier; in response to receiving a risk query request sent by a user side, the risk identification system determines risk data corresponding to a risk main body identifier carried in the risk query request in local data, and returns a query result including a risk level in the determined risk data to the user side. Since some of the specific implementation steps and related functions have been described above, they will not be described in detail here.

The data gateway in fig. 4 is used to preprocess the raw risk data before the data provider chains it up, resulting in risk data in a standardized format. After the risk identification system obtains the contribution degrees of the data providers, value differentiation can be performed through interaction between the data gateway and the data providers.

In the embodiment of the present invention, after returning the query result to the user side, the risk identification system receives feedback information for the query result sent by the user side, determines value data of a single query of the user side according to a pre-stored historical transaction record of the user side, determines the contribution degree of each data provider according to the number of data providers providing risk levels in the query result and the feedback information, and performs a lubrication operation on each data provider based on the value data and the contribution degree of each data provider; wherein the historical transaction record is written into the block chain in advance by the risk identification system, and the historical transaction record comprises: user identification, transaction amount, and total number of queries.

As a preferred scheme, after the query result is returned to the user side, the risk identification system generates a corresponding query record and writes the query record into the block chain, so that the data provider can query the block chain; the query record comprises: the risk query system comprises a user identifier, a risk main body identifier carried in the risk query request, the query result, an identifier of a data provider providing risk level in the query result and query time.

Preferably, after receiving the feedback information, the risk identification system generates a corresponding feedback record according to the feedback information and writes the feedback record into the block chain, so that the data provider can inquire the feedback record.

In practical application, after determining the contribution degree of each data provider, the risk identification system generates contribution degree data including an identifier of the data provider providing the risk level in the query result, the contribution degree, and the corresponding query record identifier, and writes the contribution degree data into the block chain for the data provider to query.

In a specific application, after the moisturizing operation is executed, the risk identification system generates a moisturizing record including a moisturizing result and a corresponding contribution data identifier and writes the moisturizing record into the block chain for the data provider to query.

In one embodiment, for any acquired risk data provided by any data provider: when judging that risk data which are provided by the data provider and have the same risk subject identification with the risk data do not exist locally, the risk identification system stores any risk data locally; and when judging that the risk data with the same risk subject identification as the risk data exists locally, the risk identification system replaces the risk data with the same risk subject identification as the risk data by any risk data.

In an optional implementation manner, any risk data further includes an applicable scenario of risk subject identification and risk level, and the risk query request further carries the applicable scenario; and after receiving the risk query request, the risk identification system determines the risk subject identifier carried in the risk query request and the risk data corresponding to the applicable scene in the local data.

In an optional technical solution, any risk data further includes a validity period of the risk data; after receiving the risk query request, the risk identification system determines risk subject identification carried in the risk query request and risk data which corresponds to an applicable scene and is in an effective state in local data, and takes the determined risk data as target risk data; and the risk identification system returns the query result comprising the risk grade in the target risk data to the user side.

Particularly, the risk query request further carries a false alarm tolerance; and determining the risk grade in the target risk data as the risk grade in the query result by a risk identification system under the condition that the number of the target risk data is one, or the number of the target risk data is multiple and multiple pieces of target risk data contain the same risk grade; in the case where the number of the target risk data is plural and the plural pieces of target risk data contain different risk levels: if the false alarm tolerance represents high false alarm tolerance, the risk identification system determines the highest risk grade in the different risk grades as the risk grade in the query result; and if the false alarm tolerance represents low false alarm tolerance, determining the lowest risk grade in the different risk grades as the risk grade in the query result by the risk identification system.

In a specific scenario, after the query result is returned to the user side, the risk identification system determines value data of single query of the user side according to a pre-stored historical transaction record of the user side, determines contribution degrees of data providers according to the number of data providers providing risk levels in the query result, and performs a moisturizing operation on the data providers based on the value data and the contribution degrees of the data providers.

In addition, in the embodiment of the present invention, the blockchain is maintained by the multiple data providers, the risk data further includes a data source and a risk tag characterizing a risk type, the query result further includes the risk tag, and the risk subject identifier includes a mobile terminal number and/or an IP address; the risk identification system receives a plurality of pieces of risk data pushed by the block chain through executing a pre-deployed intelligent contract; the intelligent contract is used for pushing when the block chain has newly added or updated risk data; and in response to the received risk query request, the risk identification system returns information without a matching result to the user side when determining that the target risk data does not exist in the local data.

One embodiment of the present invention is explained below.

The rapid development of the internet industry provides opportunities for the black industry to acquire violence, and the IP address and the mobile phone number are data with high correlation with risk behaviors in the links of registration, login, marketing, order and payment. Currently, each enterprise (i.e. data provider) generally performs real-time and offline risk detection and identification based on respective platform full-link service data, and performs risk portrayal on an IP address and a mobile phone number in a service by combining artificial intelligence algorithms such as machine learning, deep learning, graph calculation and the like, so as to obtain risk data.

At present, risk data of each data provider is not fused and centralized, so that a user is difficult to select accurate risk data for risk protection, and a severe challenge is formed on business wind control operation.

In this embodiment, each data provider is first invited to join the federation block chain. And each data provider preprocesses the risk data through the data gateway and links the preprocessed data on the chains through the intelligent contract. The block chain system synchronizes risk data to a risk identification system and updates the risk data in real time, the risk identification system provides services for the outside through an Open Application Programming Interface (OpenAPI), user resource package purchase records (namely historical transaction records), query records and feedback record data are linked up through an intelligent contract, contribution degrees of the risk identification systems are calculated based on the records, value moisture is divided based on the contribution degrees, and finally the contribution degree data and the moisture dividing records are linked up through the intelligent contract data, so that fairness, justness and traceability of data contribution and moisture division of each party are ensured. The specific implementation steps are as follows:

the method comprises the following steps: risk data preprocessing

And managing the alliance members of the data provider, and distributing specific roles and authorities. The data provider preprocesses the existing risk data through the data gateway, processes the existing risk data into data in a standardized format, and automatically links the processed data in an intelligent contract mode, wherein the data comprises dimensions such as risk main body identification, risk level, risk label, data source, applicable scene, validity period and the like.

Step two: risk data synchronization and update

The alliance blockchain system manages uplink data in real time through intelligent contract management and blockchain management, and synchronizes the latest risk data to the risk identification system for being called by an external user.

Step three: risk intelligence data invocation

And (3) cost management: the risk identification system provides service for the outside through OpenAPI, and the user uses the risk identification service in a mode of purchasing a resource package. And linking the historical transaction records through the intelligent contract.

And (3) authentication management: and the user accesses the API interface through the Access Key ID and the Access Key Secret authentication mode.

Flow limiting management: and the flow restriction management is carried out in a token distribution mode, so that the user is prevented from abusing the interface.

Risk inquiry: and the user side submits the ciphertext data, the wind control scene and the misinformation tolerance generated by the mobile phone number or the IP address in an MD5 or other appointed modes to the risk identification system through the API interface, and the risk identification system returns a risk level and a risk label.

For the same mobile phone number or IP, the risk level and the applicable scene given by each data provider are possibly inconsistent, and under the condition that the validity period and the applicable scene are both satisfied, the risk identification system flexibly makes a reference according to the misinformation tolerance of the user: when the tolerance of the false alarm is high, the risk identification system outputs the highest risk level; and when the tolerance of the false alarm is low, the risk identification system outputs the lowest risk level.

Step four: contribution degree calculation

For a single query, if the risk levels of the data providers are the same, calculating respective contribution degrees in an average manner; if the risk levels of the data providers are different, but the user selects low false alarm tolerance and outputs the lowest risk level, calculating the respective contribution degrees in an average mode; if the risk levels of the data providers are different, the user selects high false alarm tolerance and outputs the highest risk level, and meanwhile false alarm feedback is obtained, the respective contribution degrees are reduced by 10% on the basis of averagely calculating the respective contribution degrees. And finally, linking the contribution data through the intelligent contract.

Step five: business risk information value distribution

And the risk identification system calculates the cost of single inquiry of the mobile phone number or the IP address through the resource package purchase record of the user and performs lubrication based on the contribution degree. And finally, linking the distribution records through the intelligent contract.

In the technical scheme of the embodiment of the invention, the multi-party risk data is fused, shared and unified and integrated through the block chain alliance technology, and then the service is provided to the outside through the API. By linking risk data, historical transaction records, query records, feedback records, contribution degree data, distribution records and the like of the user, the data management cost of each data provider can be reduced on the premise of ensuring data safety and traceability. In addition, the risk identification system can ensure the data contribution of each party and the fairness, justness and traceability of value moisture in a reasonable contribution degree calculation and moisture dividing mode, and complete the efficient circulation of the risk data value on the premise of ensuring the data ownership of each party.

Fig. 5 shows an exemplary system architecture 500 to which the risk data processing method or risk data processing apparatus of an embodiment of the invention may be applied.

As shown in fig. 5, the system architecture 500 may include

terminal devices

501, 502, 503, a network 504, and a server 505 (this architecture is merely an example, and the components included in a particular architecture may be adapted according to application specific circumstances). The network 504 serves to provide a medium for communication links between the

terminal devices

501, 502, 503 and the server 505. Network 504 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.

A user may use

terminal devices

501, 502, 503 to interact with a server 505 over a network 504 to receive or send messages, etc. Various client applications, such as risk identification applications, etc. (for example only) may be installed on the

terminal devices

501, 502, 503.

The

terminal devices

501, 502, 503 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.

The server 505 may be a server providing various services, such as a risk identification server (for example only) providing support for risk identification applications operated by users with the

terminal devices

501, 502, 503. The risk identification server may process the received risk query request and feed back the processing results (e.g. query results including risk level-by way of example only) to the

terminal devices

501, 502, 503.

It should be noted that the risk data processing method provided by the embodiment of the present invention is generally executed by the server 505, and accordingly, the risk data processing apparatus is generally disposed in the server 505.

It should be understood that the number of terminal devices, networks, and servers in fig. 5 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for an implementation.

The invention also provides the electronic equipment. The electronic device of the embodiment of the invention comprises: one or more processors; and the storage device is used for storing one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors realize the risk data processing method provided by the invention.

Referring now to FIG. 6, shown is a block diagram of a computer system 600 suitable for use with the electronic device implementing an embodiment of the present invention. The electronic device shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.

As shown in fig. 6, the computer system 600 includes a Central Processing Unit (CPU) 601 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. In the RAM603, various programs and data necessary for the operation of the computer system 600 are also stored. The CPU601, ROM 602, and RAM603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.

The following components are connected to the I/O interface 605: an input portion 606 including a keyboard, a mouse, and the like; an output portion 607 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The driver 610 is also connected to the I/O interface 605 as needed. A removable medium 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 610 as necessary, so that the computer program read out therefrom is mounted in the storage section 608 as necessary.

In particular, the processes described in the main step diagrams above may be implemented as computer software programs, according to embodiments of the present disclosure. For example, embodiments of the invention include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the main step diagram. In the above-described embodiment, the computer program may be downloaded and installed from a network through the communication section 609, and/or installed from the removable medium 611. The computer program performs the above-described functions defined in the system of the present invention when executed by the central processing unit 601.

It should be noted that the computer readable medium shown in the present invention can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units described in the embodiments of the present invention may be implemented by software or hardware. The described units may also be provided in a processor, and may be described as: a processor includes a data synchronization unit and an external service unit. Where the names of these units do not in some cases constitute a limitation of the unit itself, for example, a data synchronization unit may also be described as a "unit providing risk data to an external service unit".

As another aspect, the present invention also provides a computer-readable medium, which may be contained in the apparatus described in the above embodiments; or may be separate and not incorporated into the device. The computer readable medium carries one or more programs which, when executed by the apparatus, cause the apparatus to perform steps comprising: acquiring a plurality of pieces of risk data from a preset block chain and storing the risk data in the local; the risk data is written into the block chain in advance by a plurality of data providers, and any risk data provided by any data provider comprises a risk subject identifier and a risk level determined by the data provider for the risk subject identifier; and in response to receiving a risk query request sent by a user side, determining risk data corresponding to a risk main body identifier carried in the risk query request in local data, and returning a query result including a risk grade in the determined risk data to the user side.

The above-described embodiments should not be construed as limiting the scope of the invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions can occur, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method of risk data processing, comprising:

acquiring a plurality of pieces of risk data from a preset block chain and storing the risk data in the local; the risk data are written into the block chain in advance by a plurality of data providers, and any risk data provided by any data provider comprises a risk subject identifier and a risk level determined by the data provider for the risk subject identifier;

and in response to receiving a risk query request sent by a user side, determining risk data corresponding to a risk main body identifier carried in the risk query request in local data, and returning a query result including a risk grade in the determined risk data to the user side.

2. The method of claim 1, wherein obtaining and storing the plurality of risk data from the predetermined blockchain locally comprises:

for any acquired risk data provided by any data provider: when judging that risk data which are provided by the data provider and have the same risk subject identification with the risk data do not exist locally, storing any risk data locally;

and when the risk data with the same risk subject identification as the risk data exists locally, replacing the risk data with the same risk subject identification as the risk data by any risk data.

3. The method according to claim 1, wherein any risk data further includes an applicable scenario of risk subject identification and risk level, and the risk query request further carries the applicable scenario; and determining, in the local data, risk data corresponding to the risk subject identifier carried in the risk query request, including:

and determining the risk subject identification carried in the risk query request and the risk data corresponding to the applicable scene in the local data.

4. The method of claim 3, wherein any risk data further includes a validity period for the risk data; and determining, in the local data, risk data corresponding to the risk subject identifier carried in the risk query request, and returning a query result including the risk level in the determined risk data to the user side, including:

determining risk data which corresponds to the risk subject identification and the applicable scene carried in the risk query request and is in an effective state in local data, and taking the determined risk data as target risk data;

and returning the query result comprising the risk level in the target risk data to the user side.

5. The method of claim 4, wherein the risk query request further carries a false positive tolerance; and returning a query result including the risk level in the target risk data to the user side, including:

determining the risk level in the target risk data as the risk level in the query result under the condition that the number of the target risk data is one, or the number of the target risk data is multiple and multiple pieces of target risk data contain the same risk level;

in the case where the number of the target risk data is plural and the plural pieces of target risk data contain different risk levels: if the false alarm tolerance represents high false alarm tolerance, determining the highest risk grade in the different risk grades as the risk grade in the query result; and if the false alarm tolerance represents low false alarm tolerance, determining the lowest risk grade in the different risk grades as the risk grade in the query result.

6. The method of claim 5, further comprising:

after the query result is returned to the user side, determining the value data of the user side single query according to the pre-stored historical transaction record of the user side;

determining contribution degrees of the data providers according to the number of the data providers providing the risk levels in the query result;

and performing a lubricating operation to each data provider based on the value data and the contribution degree of each data provider.

7. The method of claim 5, further comprising:

after the query result is returned to the user side, receiving feedback information aiming at the query result sent by the user side;

determining value data of single query of the user side according to a pre-stored historical transaction record of the user side;

determining the contribution degree of each data provider according to the number of the data providers providing the risk level in the query result and the feedback information;

8. The method of claim 7, wherein the historical transaction record is written into the blockchain in advance, and wherein the historical transaction record comprises: user identification, transaction amount and total number of queries; and, the method further comprises:

after the query result is returned to the user side, generating a corresponding query record and writing the query record into the block chain for the query of the data provider; the query record comprises: the user identification, the risk subject identification carried in the risk query request, the query result, the identification of a data provider providing the risk level in the query result and the query time;

and after the feedback information is received, generating a corresponding feedback record according to the feedback information and writing the feedback record into the block chain for the query of the data provider.

9. The method of claim 8, further comprising:

after determining the contribution degree of each data provider, generating contribution degree data comprising the identifier of the data provider providing the risk level in the query result, the contribution degree and the corresponding query record identifier, and writing the contribution degree data into the blockchain for the query of the data provider;

after the dividing operation is executed, a dividing record comprising a dividing result and the corresponding contribution degree data identification is generated and written into the block chain for the data provider to inquire.

10. The method according to any of claims 4-9, wherein the blockchain is maintained by the plurality of data providers, the risk data further comprises a data source and a risk label characterizing a risk type, the query result further comprises the risk label, and the risk subject identifier comprises a mobile terminal number and/or an IP address;

the acquiring a plurality of risk data from a preset block chain and storing the risk data locally comprises: receiving a plurality of pieces of risk data pushed by the blockchain through execution of a pre-deployed intelligent contract; the intelligent contract is used for pushing when the block chain has newly increased or updated risk data;

the method further comprises: and responding to the received risk query request, and returning information without a matching result to the user side when the target risk data does not exist in the local data.

11. A risk data processing apparatus, comprising:

the data synchronization unit is used for acquiring a plurality of pieces of risk data from a preset block chain and storing the risk data locally; the risk data is written into the block chain in advance by a plurality of data providers, and any risk data provided by any data provider comprises a risk subject identifier and a risk level determined by the data provider for the risk subject identifier;

an external service unit for: and responding to a risk query request sent by a user side, determining risk data corresponding to a risk main body identifier carried in the risk query request in local data, and returning a query result including a risk grade in the determined risk data to the user side.

12. A risk data processing system, comprising: a plurality of data providers, a blockchain maintained by the plurality of data providers, and a risk identification system;

the risk identification system acquires a plurality of pieces of risk data from a preset block chain and stores the risk data in the local; the risk data are written into the block chain in advance by the plurality of data providers, and any risk data provided by any data provider comprises a risk subject identifier and a risk level determined by the data provider for the risk subject identifier;

in response to receiving a risk query request sent by a user side, the risk identification system determines risk data corresponding to a risk main body identifier carried in the risk query request in local data, and returns a query result including a risk level in the determined risk data to the user side.

13. The risk data processing system according to claim 12, wherein the risk identification system receives feedback information for the query result sent by the user terminal after returning the query result to the user terminal, determines value data of a single query from a pre-stored historical transaction record of the user terminal, determines contribution degrees of each data provider according to the number of data providers providing risk levels in the query result and the feedback information, and performs a relegation operation to each data provider based on the value data and the contribution degrees of each data provider; wherein the historical transaction record is written into the block chain by the risk identification system in advance, and the historical transaction record comprises: user identification, transaction amount and total number of inquiry times;

after the query result is returned to the user side, the risk identification system generates a corresponding query record and writes the query record into the block chain so as to be queried by the data provider; the query record comprises: the user identification, the risk subject identification carried in the risk query request, the query result, the identification of a data provider providing the risk level in the query result and the query time;

after receiving the feedback information, the risk identification system generates a corresponding feedback record according to the feedback information and writes the feedback record into the block chain for the data provider to inquire;

after determining the contribution degree of each data provider, the risk identification system generates contribution degree data including an identifier of the data provider providing the risk level in the query result, the contribution degree and a corresponding query record identifier, and writes the contribution degree data into the blockchain for the data provider to query;

after the dividing and lubricating operation is executed, the risk identification system generates a dividing and lubricating record comprising a dividing and lubricating result and the corresponding contribution degree data identification and writes the dividing and lubricating record into the block chain for the data provider to inquire.

14. An electronic device, comprising:

one or more processors;

a storage device to store one or more programs,

when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-10.

15. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-10.