CN117874791A - Risk assessment method and device for information query and electronic equipment - Google Patents

Risk assessment method and device for information query and electronic equipment Download PDF

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Publication number
CN117874791A
CN117874791A CN202410108052.4A CN202410108052A CN117874791A CN 117874791 A CN117874791 A CN 117874791A CN 202410108052 A CN202410108052 A CN 202410108052A CN 117874791 A CN117874791 A CN 117874791A
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information
risk
product
query
client
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刘认伦
田绍亮
张悦宁
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China Telecom Corp Ltd
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China Telecom Corp Ltd
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Abstract

The invention discloses a risk assessment method and device for information query and electronic equipment, and relates to the field of information security, wherein the risk assessment method comprises the following steps: receiving an information inquiry request sent by a user side, and analyzing the information inquiry request to obtain input information; calculating a first risk value corresponding to the input information; receiving a query result returned by the server, and analyzing the query result to obtain result information; calculating a second risk value corresponding to the result information; and obtaining a risk vector based on the first risk value and the second risk value, and performing risk assessment on the risk vector to obtain risk information. The invention solves the technical problems that in the related technology, when a user side inquires information in a system, an effective risk assessment method is lacked, the information security is low, and information disclosure is easy to cause.

Description

Risk assessment method and device for information query and electronic equipment
Technical Field
The invention relates to the field of information security, in particular to a risk assessment method and device for information query and electronic equipment.
Background
With the rapid development of big data technology, large enterprises or companies often adopt a unified service system to conduct data storage and service handling, a large amount of enterprise asset data, customer data and product data are involved in the service system, the involved data information range is wide, the data volume is large, when the user ends of different agents need to inquire service data from the service system, the risk of malicious competition and information disclosure in the enterprise is easily caused due to low information security caused by lack of an effective inquiry behavior risk assessment method.
In view of the above problems, no effective solution has been proposed at present.
Disclosure of Invention
The embodiment of the invention provides a risk assessment method and device for information query and electronic equipment, which are used for at least solving the technical problems that in the related technology, when a user side queries information in a system, an effective risk assessment method is lacked, the information security is low and information leakage is easy to cause.
According to an aspect of the embodiment of the present invention, there is provided a risk assessment method for information query, including: receiving an information inquiry request sent by a user side, and analyzing the information inquiry request to obtain input information, wherein the information inquiry request is used for inquiring customer information and product information corresponding to products handled by a customer; calculating a first risk value corresponding to the input information; receiving a query result returned by a server, and analyzing the query result to obtain result information; calculating a second risk value corresponding to the result information; and obtaining a risk vector based on the first risk value and the second risk value, and performing risk assessment on the risk vector to obtain risk information.
Optionally, the step of calculating a first risk value corresponding to the input information includes: acquiring a client risk coefficient corresponding to the client information based on the client information in the input information, wherein the client risk coefficient is a weight value corresponding to the client information input by the client on a visual interface; calculating a product risk coefficient based on the product information in the input information; extracting inquiry time information in the input information, and acquiring a time risk coefficient corresponding to the inquiry time information, wherein the time risk coefficient is a weight value corresponding to the inquiry time information; and calculating a first risk value of the input information based on the customer risk coefficient, the product risk coefficient and the moment risk coefficient.
Optionally, the step of calculating the product risk factor based on the product information in the input information comprises: acquiring a first product weight value corresponding to the product information based on the product information in the input information; inquiring a relevance dictionary based on a user identifier of the user side and a product identifier in the product information to obtain a first product relevance value between the user side and the product information, wherein the relevance dictionary is pre-constructed and used for recording the relevance value among the user side, the client information and the product information, and the relevance value is used for representing the matching degree among the user, the client and the product; and calculating the product risk coefficient based on a first product weight value corresponding to the product information and a first product correlation value between the user side and the product information.
Optionally, the step of acquiring the time risk coefficient corresponding to the query time information based on the query time information in the input information includes: judging the inquiry time in the inquiry time information to obtain a judging result; acquiring a weight value of business hours as a time risk coefficient corresponding to the query time information under the condition that the determination result indicates that the query time is the business hours; and under the condition that the judging result indicates that the inquiring time is non-business time, acquiring a weight value of the non-business time as a time risk coefficient corresponding to the inquiring time information.
Optionally, the step of calculating a second risk value corresponding to the result information includes: calculating a risk coefficient of a query client corresponding to the client information in the result information; calculating a risk coefficient of the query product corresponding to the product information in the result information; and calculating a second risk value corresponding to the result information based on the risk coefficient of the query customer, the risk coefficient of the query product and the moment risk coefficient.
Optionally, the step of calculating the risk coefficient of the querying client corresponding to the client information in the result information includes: acquiring a weight value corresponding to the client information based on the client information in the result information; inquiring a relevance dictionary based on the user identification of the user side and the client identification in the client information to obtain a relevance value between the user side and the client information; and calculating a query client risk coefficient corresponding to the client information in the result information based on the weight value corresponding to the client information and the correlation value between the client and the client information.
Optionally, the step of calculating the risk coefficient of the query product corresponding to the product information in the result information includes: acquiring a second product weight value corresponding to the product information based on the product information in the result information; inquiring a relevance dictionary based on the user identification of the user side and the product identification in the product information to obtain a second product relevance value between the user side and the product information; and calculating a risk coefficient of the query product corresponding to the product information in the result information based on the first product weight value corresponding to the product information and the second product correlation value between the user side and the product information.
Optionally, obtaining a risk vector based on the first risk value and the second risk value, and performing risk assessment on the risk vector, where the step of obtaining risk information includes: splicing the first risk value and the second risk value to obtain a risk vector; based on the risk vector matching vector interval, obtaining a risk level corresponding to the risk vector; and configuring a processing strategy for the information query request and the query result of the user terminal based on the risk grade, generating risk information and returning the risk information to the user terminal.
According to another aspect of the embodiment of the present invention, there is also provided a risk assessment device for information query, including: the first analysis unit is used for receiving an information inquiry request sent by a user side, and analyzing the information inquiry request to obtain input information, wherein the information inquiry request is used for inquiring customer information and product information corresponding to products handled by a customer; the first calculating unit is used for calculating a first risk value corresponding to the input information; the second analysis unit is used for receiving the query result returned by the server and analyzing the query result to obtain result information; the second calculating unit is used for calculating a second risk value corresponding to the result information; the evaluation unit is used for obtaining a risk vector based on the first risk value and the second risk value, and performing risk evaluation on the risk vector to obtain risk information.
Optionally, the first computing unit includes: the first acquisition module is used for acquiring a client risk coefficient corresponding to the client information based on the client information in the input information, wherein the client risk coefficient is a weight value corresponding to the client information input by the client at a visual interface; the first calculation module is used for calculating a product risk coefficient based on the product information in the input information; the second acquisition module is used for extracting inquiry time information in the input information and acquiring a time risk coefficient corresponding to the inquiry time information, wherein the time risk coefficient is a weight value corresponding to the inquiry time information; and the second calculation module is used for calculating a first risk value of the input information based on the client risk coefficient, the product risk coefficient and the moment risk coefficient.
Optionally, the first computing module includes: the first acquisition sub-module is used for acquiring a first product weight value corresponding to the product information based on the product information in the input information; the first query sub-module is used for querying a correlation dictionary based on the user identification of the user terminal and the product identification in the product information to obtain a first product correlation value between the user terminal and the product information, wherein the correlation dictionary is pre-constructed and used for recording the correlation value among the user terminal, the client information and the product information, and the correlation value is used for representing the matching degree among the user, the client and the product; and the first calculating sub-module is used for calculating the product risk coefficient based on a first product weight value corresponding to the product information and a first product correlation value between the user side and the product information.
Optionally, the first acquisition module includes: the first judging submodule is used for judging the inquiry time in the inquiry time information to obtain a judging result; the second obtaining submodule is used for obtaining a weight value of business hours as a time risk coefficient corresponding to the query time information when the determination result indicates that the query time is the business hours; and the third acquisition sub-module is used for acquiring a weight value of the non-business time as a time risk coefficient corresponding to the query time information under the condition that the determination result indicates that the query time is the non-business time.
Optionally, the second computing unit includes: the third calculation module is used for calculating a risk coefficient of the inquiring client corresponding to the client information in the result information; the fourth calculation module is used for calculating a risk coefficient of the query product corresponding to the product information in the result information; and a fifth calculation module, configured to calculate a second risk value corresponding to the result information based on the risk coefficient of the query customer, the risk coefficient of the query product, and the time risk coefficient.
Optionally, the third computing module includes: a fourth obtaining sub-module, configured to obtain a weight value corresponding to the client information based on the client information in the result information; the second query sub-module is used for querying a relevance dictionary based on the user identification of the user terminal and the client identification in the client information to obtain a relevance value between the user terminal and the client information; and the second calculation sub-module is used for calculating the risk coefficient of the inquiring client corresponding to the client information in the result information based on the weight value corresponding to the client information and the correlation value between the client and the client information.
Optionally, the fourth computing module includes: a fifth obtaining sub-module, configured to obtain a second product weight value corresponding to the product information based on the product information in the result information; the third query sub-module is used for querying a relevance dictionary based on the user identification of the user end and the product identification in the product information to obtain a second product relevance value between the user end and the product information; and the third calculation sub-module is used for calculating a risk coefficient of the query product corresponding to the product information in the result information based on the first product weight value corresponding to the product information and the second product correlation value between the user side and the product information.
Optionally, the evaluation unit includes: the first splicing module is used for splicing the first risk value and the second risk value to obtain a risk vector; the first matching module is used for matching the vector interval based on the risk vector to obtain a risk level corresponding to the risk vector; the first generation module is used for configuring a processing strategy for the information query request and the query result of the user terminal based on the risk grade, and generating risk information and returning the risk information to the user terminal.
According to another aspect of the embodiment of the present invention, there is also provided an electronic device, including one or more processors and a memory, where the memory is configured to store one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors are caused to implement a risk assessment method for any one of the above information queries.
In the present disclosure, the method comprises the following steps: the method comprises the steps of receiving an information inquiry request sent by a user side, analyzing the information inquiry request to obtain input information, calculating a first risk value corresponding to the input information, receiving an inquiry result returned by a server, analyzing the inquiry result to obtain result information, calculating a second risk value corresponding to the result information, obtaining a risk vector based on the first risk value and the second risk value, and performing risk assessment on the risk vector to obtain risk information.
In the disclosure, an information query request of a user side and a query result returned by a server are both analyzed, risk value calculation is performed on input information and result information obtained through analysis, and then a risk vector of the query behavior of the user side is obtained, the risk degree of the query behavior of the user side can be defined in a digital form according to the risk vector, different solutions are provided according to different risk degrees, the query behavior with risks is prevented in time, the problem of information leakage is avoided, the information safety is protected, and further the technical problems that in related technologies, an effective risk assessment method is lacking when the user side queries information in a system, the information safety is low, and information leakage is easy to cause are solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiments of the invention and together with the description serve to explain the invention and do not constitute a limitation on the invention. In the drawings:
FIG. 1 is a flow chart of an alternative risk assessment method for information queries in accordance with an embodiment of the present invention;
FIG. 2 is a schematic diagram of a risk assessment process for an alternative information query according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of an alternative risk assessment system for information queries in accordance with an embodiment of the present invention;
FIG. 4 is a schematic diagram of an alternative risk assessment device for information query according to an embodiment of the present invention;
fig. 5 is a block diagram of a hardware structure of an electronic device (or a mobile device) of a risk assessment method of information query according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be noted that, the risk assessment method and the apparatus for information query in the present disclosure may be used in the case where the information security domain performs risk assessment on the query behavior of the user side, and may also be used in any domain other than the information security domain, where the risk assessment is performed on the query behavior of the user side, where the application domain of the risk assessment method and the apparatus for information query in the present disclosure is not limited.
It should be noted that, related information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, presented data, etc.) related to the present disclosure are information and data authorized by a user or sufficiently authorized by each party, and the collection, use and processing of related data need to comply with related laws and regulations and standards of related areas, and are provided with corresponding operation entries for the user to select authorization or rejection. For example, an interface is provided between the system and the relevant user or institution, before acquiring the relevant information, the system needs to send an acquisition request to the user or institution through the interface, and acquire the relevant information after receiving the consent information fed back by the user or institution.
The following embodiments of the present invention are applicable to risk assessment systems/applications/devices for various query behaviors. According to the invention, the information inquiry request sent by the user side and the inquiry result returned by the server are analyzed, so that risk calculation is carried out on input information and result information obtained by analysis, a risk vector of the inquiry behavior is obtained, the risk grade of the inquiry behavior can be estimated through the risk vector, and further, the inquiry behavior with risk is intervened in time, thereby protecting information security and improving the security of the system.
The present invention will be described in detail with reference to the following examples.
Example 1
According to an embodiment of the present invention, there is provided an embodiment of a risk assessment method for information query, it should be noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system such as a set of computer executable instructions, and that although a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different from that herein.
FIG. 1 is a flowchart of an alternative risk assessment method for information queries, as shown in FIG. 1, according to an embodiment of the present invention, the method comprising the steps of:
Step S101, receiving an information inquiry request sent by a user side, and analyzing the information inquiry request to obtain input information, wherein the information inquiry request is used for inquiring customer information and product information corresponding to products handled by customers;
step S102, calculating a first risk value corresponding to input information;
step S103, receiving a query result returned by the server, and analyzing the query result to obtain result information;
step S104, calculating a second risk value corresponding to the result information;
step S105, a risk vector is obtained based on the first risk value and the second risk value, and risk assessment is carried out on the risk vector to obtain risk information.
Through the steps, an information inquiry request sent by a user side is received, the information inquiry request is analyzed to obtain input information, a first risk value corresponding to the input information is calculated, an inquiry result returned by a server is received, the inquiry result is analyzed to obtain result information, a second risk value corresponding to the result information is calculated, finally a risk vector is obtained based on the first risk value and the second risk value, and risk assessment is performed on the risk vector to obtain risk information.
In this embodiment, the information query request of the user side and the query result returned by the server are both parsed, and the risk value is calculated on the input information and the result information obtained by the parsing, so as to obtain a risk vector of the query behavior of the user side, according to the risk vector, the risk degree of the query behavior of the user side can be defined in a digital form, and according to different risk degrees, different solutions are provided, so that the query behavior with risk is prevented in time, the information leakage problem is avoided, the information security is protected, and further the technical problems that in the related technology, when the user side queries the information in the system, an effective risk assessment method is lacking, the information security is low, and information leakage is easy to cause are solved.
Embodiments of the present invention will be described in detail with reference to the following steps.
It should be noted that, the embodiment of the present invention may be applied to various query systems or service systems with query functions, where a large amount of customer information and product information are stored in a service system of a large enterprise or company, and the service system is opened to different branch companies or agents, and users of the branch companies or agents may query relevant information in the service system in real time, so as to learn about the business handling situation or product purchasing situation of the customer, for example, the situation that the customer handles broadband, the customer communication charge situation, the customer class situation, and so on. In the practical use process, the attribute of the system user and the searched asset data and customer data belonging to different units can be inevitably generated, and when the correlation degree of the attribute of the system user and the searched data is large, malicious competition in an enterprise can be possibly caused, confidential leakage of the enterprise can be even further caused, and irrecoverable loss can be caused. However, in order to develop the business normally, the system user is allowed to inquire the clients or products different from the self unit, so that a set of method is needed to judge the rationality of the inquiry behavior of the user terminal, and the malicious competition and the information leakage risk inside the enterprise are avoided.
Before risk assessment is performed on query behavior, the embodiment of the invention needs to configure a correlation value between a user side and client information and product information, and construct a correlation dictionary based on the correlation value, for example, the correlation between a user side role and a client information type, the correlation between a unit to which the user side belongs and a product landing grid and a product cooperation unit, and the like.
It should be noted that, in the embodiment of the invention, risk coefficients are set at the user end and the server end respectively, and the risk coefficients are set at the input end, so that malicious inquiry of product information and malicious competition of friends by a user can be early warned, and the risk coefficients of inquiry results are set at the server end, so that leakage of client information and exposure of sensitive information can be early warned, and further information safety in a system can be protected.
Step S101, receiving an information inquiry request sent by a user side, and analyzing the information inquiry request to obtain input information.
It should be noted that, the user side may be an agent user side or an enterprise division user side, where the user side inputs related information, such as a customer name, a customer phone number, etc., on the input interface of the system based on its own demand or customer demand, and then sends an information query request to a server of the service system to request for obtaining the related information, where a risk assessment module of the information query is disposed between the client side and the server, and may obtain the information query request sent by the user side and a query result returned by the server at the same time, and analyze and calculate the information query request, where the information query request is used to query customer information and product information corresponding to a product handled by the customer.
Step S102, calculating a first risk value corresponding to the input information.
After receiving the information inquiry request, the information inquiry request is analyzed to obtain input information, wherein the input information is information input by a user on a visual interface of a user side, and the information can be used as inquiry conditions to inquire related information from a server.
Optionally, the step of calculating a first risk value corresponding to the input information includes: acquiring a client risk coefficient corresponding to the client information based on the client information in the input information, wherein the client risk coefficient is a weight value corresponding to the client information input by a user terminal on a visual interface; calculating a product risk coefficient based on the product information in the input information; extracting inquiry time information in input information, and acquiring a time risk coefficient corresponding to the inquiry time information, wherein the time risk coefficient is a weight value corresponding to the inquiry time information; a first risk value of the input information is calculated based on the customer risk coefficient, the product risk coefficient, and the time of day risk coefficient.
It should be noted that, the input risk is calculated according to the information input by the user side, and the input risk includes three parts: when calculating a first risk value of input information, firstly acquiring a weight value corresponding to the information according to the input client information, taking the weight value as a client risk coefficient, then acquiring the weight value corresponding to the information based on the product information in the input information, taking the weight value as a product risk coefficient, and finally acquiring the weight values corresponding to the information at different moments through the inquiring moment.
Optionally, the step of calculating the product risk factor based on the product information in the input information comprises: acquiring a first product weight value corresponding to the product information based on the product information in the input information; inquiring a relevance dictionary based on a user identifier of a user terminal and a product identifier in product information to obtain a first product relevance value between the user terminal and the product information, wherein the relevance dictionary is constructed in advance and used for recording the relevance value among the user terminal, the client information and the product information, and the relevance value is used for representing the matching degree among the user, the client and the product; and calculating a product risk coefficient based on the first product weight value corresponding to the product information and the first product correlation value between the user side and the product information.
It should be noted that, the product risk coefficient is a ratio of the first product weight value to the first product correlation value, when calculating the product risk coefficient, the first product weight value corresponding to the product information is firstly obtained according to the product information in the input information, then the correlation dictionary is queried according to the user identifier of the user terminal and the product identifier recorded in the product information, the first product correlation value between the user terminal and the product information is obtained, and then the product risk coefficient is obtained according to the ratio of the first product weight value to the first product correlation value.
Optionally, the step of acquiring the time risk coefficient corresponding to the query time information based on the query time information in the input information includes: judging the inquiry time in the inquiry time information to obtain a judging result; when the judging result indicates that the inquiring time is business time, acquiring a weight value of the business time as a time risk coefficient corresponding to the inquiring time information; and when the judging result indicates that the query time is the non-business time, acquiring a weight value of the non-business time as a time risk coefficient corresponding to the query time information.
It should be noted that, the query time may be used as a parameter for evaluating the query risk, and the query time is divided into a query time in business hours and a query time in non-business hours, and generally, the risk of executing the query operation in business hours is smaller than that in non-business hours.
It should be noted that, in the embodiment of the present invention, risk assessment is performed on query behavior by using a risk vector, where the risk vector includes an input risk and a query risk, the input risk represents a risk existing in information input by a user terminal, and the query risk identifies a risk existing in a query result returned by a server, where the input risk may be represented as:
w i (C Input device ) Representing different C's for customer risk factors Input device Corresponding weight value, C Input device Including customer information entered by end user U, such as: certificate number, customer identification, order stream, etc.;is a risk factor of the product, wherein w j (P Input device ) Representing different product information P Input device Corresponding risk weight value (i.e. first product weight value), P Input device The product information input by the user terminal U can be an access number, a number grade and the like; ρ (P) Input device U) represents the user terminal U and the input product information P Input device The correlation value between the user information and the product information (namely the first product correlation value) mainly refers to the correlation between the user attribution unit and the product landing grid and the product cooperation unit, and as different products correspond to different client managers and different areas, the higher the matching degree between the user information and the product information is, the higher the correlation is, and the smaller the corresponding product risk coefficient is; w (w) k And (T) is a risk coefficient of the query time, and represents weight values corresponding to different query times T, wherein T represents the query time, and the query time is divided into business hours and non-business hours, and the risk of the business hours is generally considered to be smaller than that of the non-business hours.
Step S103, receiving the query result returned by the server, and analyzing the query result to obtain result information.
It should be noted that, in the embodiment of the present invention, in addition to risk assessment needs to be performed on the input information, risk assessment needs to be performed on the query result returned by the server, so as to determine whether the query result is within the range of reasonably acquiring information by the role to which the user side belongs.
Step S104, calculating a second risk value corresponding to the result information.
Optionally, the step of calculating the second risk value corresponding to the result information includes: calculating a risk coefficient of the inquiring client corresponding to the client information in the result information; calculating a risk coefficient of the query product corresponding to the product information in the result information; and calculating a second risk value corresponding to the result information based on the risk coefficient of the query customer, the risk coefficient of the query product and the moment risk coefficient.
It should be noted that, the second risk value corresponding to the result information includes the following three parts: the risk factors of the query clients and the risk factors of the query products are calculated, so that when the second risk value is calculated, the risk factors of the query clients corresponding to the client information in the result information are required to be obtained first, the corresponding risk factors of the query products are obtained according to the product information in the result information, and finally the second risk value corresponding to the result information is calculated according to the risk factors of the query clients and the risk factors of the query products, and the moment information can be the moment when the client sends the information query request or the moment when the server returns the query result.
Optionally, the step of calculating the risk coefficient of the querying client corresponding to the client information in the result information includes: acquiring a weight value corresponding to the client information based on the client information in the result information; inquiring a relevance dictionary based on the user identification of the user end and the client identification in the client information to obtain a relevance value between the user end and the client information; and calculating a risk coefficient of the inquiring client corresponding to the client information in the result information based on the weight value corresponding to the client information and the correlation value between the client and the client information.
It should be noted that, the risk factor of the query client is the ratio of the weight value of the client information to the correlation value between the client and the client information, when the risk factor of the query client is calculated, the weight value of the client information in the result information is firstly obtained, then the correlation dictionary is queried according to the user identifier of the client and the client identifier, the correlation value between the client and the client information is obtained, and finally the ratio of the weight value to the correlation value is obtained, thereby obtaining the risk factor of the query client.
Optionally, the step of calculating the risk coefficient of the query product corresponding to the product information in the result information includes: acquiring a second product weight value corresponding to the product information based on the product information in the result information; inquiring a relevance dictionary based on the user identification of the user end and the product identification in the product information to obtain a second product relevance value between the user end and the product information; and calculating a risk coefficient of the query product corresponding to the product information in the result information based on the first product weight value corresponding to the product information and the second product correlation value between the user side and the product information.
It should be noted that, when the risk factor of the query product is the ratio of the second product weight value to the second product relativity value, the second product weight value corresponding to the product information is obtained according to the product information in the result information when the risk factor of the query product is calculated, then the relativity dictionary is queried based on the user identification of the user end and the product identification in the product information, the second product relativity value between the user end and the product information is obtained, and then the ratio is removed to obtain the risk factor of the query product.
Specifically, the calculation formula of the risk coefficient of the query result (i.e., the second risk value corresponding to the result information) is as follows:
the risk of the query result is also composed of 3 parts, namely, the risk of the query client, the risk of the query product and the risk of the query time.
To query customer risk factors, C Querying The client information type for indicating the inquiry of the user end U comprises the following steps: customer name, customer certificate number, customer address, customer contact phone, etc.; w (w) m (C Querying ) Representing different C Querying Corresponding risk weights (namely weight values of client information in the result information); ρ (C) Querying U) is user terminal U and inquired customer information C Querying The correlation value between the user role type and the client information (namely the correlation value between the user end and the client information in the result information) refers to the correlation between the user role type and the client information type, the client sensitivity level that different roles can contact the system is different, and the higher the matching degree between the user role type and the queried client information type is, the larger the correlation is, and the smaller the corresponding client information risk is; / >To inquire the risk coefficient of the product, P Querying The product information type for indicating the inquiry of the user terminal U comprises the following steps: product details, product installation address, product customer manager, etc.; w (w) n (P Querying ) Representing different P Querying A corresponding risk weight value (i.e., a second product weight value); ρ (P) Querying U) represents the user terminal U and the query product information P Querying The correlation value (namely the second product correlation value) between the user end attribute and the product information type can be referred to as the correlation of the user end attribute and the product information type, the sensitivity level of the product information which can be contacted by different roles in the system is different, the query risk of the user end corresponding to the different roles and the product is also different, and the higher the matching degree of the user role type and the queried product information type is, the higher the correlation is, and the lower the corresponding product information risk is.
Step S105, a risk vector is obtained based on the first risk value and the second risk value, and risk assessment is carried out on the risk vector to obtain risk information.
Optionally, the step of obtaining risk vectors based on the first risk value and the second risk value, and performing risk assessment on the risk vectors to obtain risk information includes: splicing the first risk value and the second risk value to obtain a risk vector; based on the risk vector matching vector interval, obtaining a risk level corresponding to the risk vector; and configuring a processing strategy for the information inquiry request and the inquiry result of the user terminal based on the risk level, generating risk information and returning the risk information to the user terminal.
It should be noted that, the first risk value and the second risk value obtained by calculation are spliced to obtain a risk vector, vector intervals of different levels are matched according to the risk vector to obtain a risk level corresponding to the risk vector, different risk levels are preconfigured with different processing results, for example, query results are directly intercepted for query behaviors with higher risks and alarm information is sent to a client, or for query behaviors without risks, the query results are directly returned to the client, or for query behaviors with lower risks, sensitive information screening is performed on the query results, and the screened query results are returned to the client, and meanwhile, risk alarm information is sent to the client.
The following detailed description is directed to alternative embodiments.
FIG. 2 is a schematic diagram of an alternative risk assessment procedure for an information query according to an embodiment of the present invention, where, as shown in FIG. 2, the risk assessment procedure for an information query includes:
step one, starting;
step two, information input by a user is sent;
the user inputs information submitting business system in the interface search bar, and sends information inquiry request to the server, the information processing module obtains the content in the request, and obtains the information of the user end such as the role and the attribute in the system and the inquiry time information.
Analyzing the information, and calculating a risk coefficient of the input content (namely the first risk coefficient);
analyzing the acquired content, inquiring related asset data in the system, matching corresponding client information or product information, calling a weight value database and a relativity dictionary, and calculating a risk coefficient corresponding to the user input content according to a risk calculation formula.
Step four, obtaining a query result;
after the user side sends the request, the server returns a query result, and the information processing module acquires the query result, analyzes the query result and calculates the query risk of the server side.
Analyzing the query result and calculating a risk coefficient (namely the second risk value) of the query result;
analyzing the obtained query result, querying related asset data in the system, matching corresponding client information and product information, calling a weight value database and a correlation dictionary, obtaining the weight value and the correlation value of the related information, and calculating a risk coefficient of the query result according to a risk calculation formula.
Step six, combining the calculation results to obtain a risk vector, and evaluating the risk level;
and D, forming a risk vector by the results obtained by calculation in the third step and the fifth step, storing the risk vector into a data table according to the user ID, presetting a risk threshold value by the system, comparing the result of the operation with the risk threshold value, and selecting a risk processing strategy aiming at the high risk operation exceeding the threshold value.
Step seven, selecting a processing strategy based on the risk level, and recording the risk in a risk vector table;
step eight, taking a risk vector table as a basis, carrying out multidimensional statistical data, and forming an audit log;
and step nine, ending.
Fig. 3 is a schematic diagram of an alternative risk assessment system for information query according to an embodiment of the present invention, where the risk assessment system executes the risk assessment method for information query described above, as shown in fig. 3, a user inputs a query condition (in fig. 3, a user a inputs a content, a user B inputs a content, and a user C inputs a content) in a visual interface of a user side, sends an information query request to a service acceptance system (i.e., a server) according to the query condition, and at the same time, an information processing module in the risk assessment system for information query obtains the user side inputs a content and a query result returned by the service acceptance system (in fig. 3, a user a queries a result, a user B queries a result, and a user C queries a result), performs relevance dictionary query and risk value calculation by the information processing module, and splices the calculation results to obtain risk vectors of user side query behavior (in fig. 3, a risk vector R (a), a risk vector R (B), and a risk vector R (C) are indicated by the user a risk vector, and then performs risk assessment according to the risk vectors to obtain a risk level, a matching process policy and a risk table is fed back to a risk table production audit policy.
The following list four practical application scenarios that often occur:
first, a common user terminal U 1 Inquiring product information;
user side level and rights: the common use;
the input content comprises the following steps: customer cell phone number 133;
inquiring information: 133 corresponding tariff information and product attributes;
query time: 2023, 7, 5, 14 hours 53 minutes;
analyzing the input content to obtain related parameters:
no customer credentials or ID are entered, so the customer risk factor w (C 1 input )＝0。
The number is not high-grade beautiful number, so the weight value of the first product is w j (P 1 input )＝0。
The moment risk factor is w 1 (T)＝0。
Common user terminal U 1 The attribution area is A, the product attribution area is also A, the acquired information is of a common level, and the first product correlation value of the user side and the client mobile phone number can be rho (P) 1 input ,U 1 )＝1。
The first risk value of the user input information can be obtained by the method is as follows:
the query content is client tariffs and product attributes, and the second product relevance value is ρ (P Querying U) =1, since the customer package value is high, the second risk value of the query result is:
R 1 query ＝0.6
User terminal U 1 The risk vector for a single query action is:
the system will U 1 、The corresponding relation between the query log and the query log is recorded in a data table, and the risk value is compared with a threshold value, and the system arbitrates due to lower risk coefficient The behavior is low risk, and the query result is fed back to the user.
Second, advanced user terminal U 2 And querying the client information.
User class and rights: advanced;
the input content comprises the following steps: a customer mobile phone number;
the input content comprises the following steps: customer cell phone number 133;
inquiring information: 133 customer name, certificate number;
query time: 2023, 6, 20, 18 hours and 01 minutes;
analyzing the input content to obtain related parameters:
no customer credentials or ID are entered, so the customer risk factor w (C 2 input )＝0；
The number is two-stage beautiful number, so the first product weight value of the product information in the input information is w (P 2 input )＝0.1。
The moment risk factor is w 2 (T)＝0.1。
Advanced user terminal U 2 The home area in the system is B, the home unit is provided with a number pool which is a general number pool and a B area number pool, the product home area is C, the product maintenance manager home area is C, the resource home 4G beautiful number pool, the acquired information is of a common level, and the dictionary is queried to obtain that the first product relativity value of the user side and the product information is ρ (P) 2 input ,U 2 )＝0.8。
The first risk value of the user input content is:
the query content is the name and certificate number of the client, so the risk coefficients of the client and the query client are as follows:
w(C 2 inquiry )＝0.8
The user grade and the authority are advanced, and the client information is not distinguished according to the attribution unit, so the correlation value between the user side and the inquiring client information is as follows:
ρ(C 2 inquiry ,U 2 )＝1
The product information is not queried, and the second product weight value of the query product of the user side and the product information is as follows: w (P) 2 inquiry )＝0。
The second risk value of the user query result is therefore:
user terminal U 2 The risk vector for a single query is:
the system will U 2 、And recording the corresponding relation between the query log and the data table, comparing the risk value with a threshold value, informing the risk to a user by the system due to the low risk behavior of the query, screening the sensitive information of the query result, and sending the screened sensitive information to the user side.
Third, the common user terminal U 3 Inquiring customer information;
user class and rights: a common role, temporary high-level authority;
the input content comprises the following steps: customer cell phone number 133;
inquiring information: 133 customer name, certificate number;
query time: 2023, 4, 5, 21 minutes;
analyzing the input content to obtain related parameters:
since the customer certificate or the customer identification is not input, the customer risk coefficient w (C 3 input )＝0。
The number is four-level beautiful number, so the first product weight value of the product information is w (P 3 input )＝0.7。
The moment risk factor is w 3 (T)＝0.6。
Common user terminal U 3 The attribution area is D, the product attribution area is E, the acquired information is advanced, and the dictionary is queried to obtain a first product correlation value rho (P) 3 input ,U 3 )＝0.1。
Therefore, the first risk value of the user input information is:
the query content is the customer name and the document number, so the risk coefficient of the query customer for querying the customer information is as follows:
w(C 3 inquiry )＝0.8
The user grade and the authority are common roles, the temporary high-level authority, and the obtained information has low correlation with the user identity, so that the correlation value between the user side and the client information is as follows:
ρ(C 3 inquiry ,U 3 )＝0.1
Inquiring preferential information such as product discount parameters and the like to obtain a first product weight value of the inquired product information, wherein the first product weight value is as follows: w (P) 3 inquiry )＝0.5。
The user attribution unit is different from the product attribution unit, has low association degree, but can inquire the client information, which indicates that the related authority is temporarily opened, so that the second product association degree value of the user side and the inquired product information is as follows: ρ (P) Querying ,U)＝0.2。
Therefore, the second risk value of the query result at the user end is:
user terminal U 3 The single query risk vector is:
the system will U 3 、And the corresponding relation between the query log and the data table is recorded, and the risk value is compared with the threshold value, so that the system can send out high-risk early warning to the user and intercept the query result to avoid information leakage because the input content of the query and the query result (query behavior) risk are higher.
Fourth, audit scenarios.
(1) Different user risk behaviors with home store commissions settled to the same account
Auditing finds general user U 4 、U 5 、U 6 The occurrence times of the high risk vector in the same natural month reach 5 times, 7 times and 6 times, channels of 3 user ends are different, and the commission is found to be settled into the same account by abutting a commission settlement system, and the audit result of this time will be U 4 、U 5 、U 6 And (5) corresponding to the month, the occurrence times of the risk vector, and recording the commission settlement account into an audit log.
(2) Multiple low risk query behavior for an average user
Generally, the low-risk query behavior only occurs on high-value package clients, so that the low-value risk query of the same user for more than 4 times is audited in a natural month, and certain illegal behaviors can be found. The low risk behavior can be substituted into the scenario in (1) as well, and the agent's offending behavior can be detected.
(3) Multiple medium and high risk query behavior in short time
The medium and high risk inquiry behavior is inquired in a certain time period within a natural month, the certain time period can be set to be 1 week, and if the frequency of occurrence is too high, the system can correlate the related work number with the attribution channel, store and settlement relation and record the related work number into an audit log.
And audit logs are generated through the risk vectors, so that the illegal behaviors of the user side can be well discovered, the intervention can be timely carried out, and the service safety is ensured.
In the embodiment of the invention, the information query request sent by the user side and the query result returned by the server are analyzed, so that risk calculation is performed on the input information and the result information obtained by analysis, the risk vector of the query behavior is obtained, the risk grade of the query behavior can be evaluated through the risk vector, and further the query behavior with risk is interfered in time, thereby protecting the information security and improving the security of the system.
The following describes in detail another embodiment.
Example two
The risk assessment device for information query provided in this embodiment includes a plurality of implementation units, each of which corresponds to each implementation step in the first embodiment.
Fig. 4 is a schematic diagram of an alternative risk assessment apparatus for information query according to an embodiment of the present invention, as shown in fig. 4, the risk assessment apparatus for information query includes: a first parsing unit 41, a first calculation unit 42, a second parsing unit 43, a second calculation unit 44, an evaluation unit 45, wherein,
the first parsing unit 41 is configured to receive an information query request sent by a user side, and parse the information query request to obtain input information, where the information query request is used to query customer information and product information corresponding to a product handled by a customer;
A first calculating unit 42, configured to calculate a first risk value corresponding to the input information;
a second parsing unit 43, configured to receive a query result returned by the server, and parse the query result to obtain result information;
a second calculating unit 44, configured to calculate a second risk value corresponding to the result information;
the evaluation unit 45 is configured to obtain a risk vector based on the first risk value and the second risk value, and perform risk evaluation on the risk vector to obtain risk information.
In the risk assessment device for information query, the first analysis unit 41 receives an information query request sent by a user side, and analyzes the information query request to obtain input information, where the information query request is used for querying customer information and product information corresponding to products handled by the customer; calculating a first risk value corresponding to the input information by the first calculating unit 42; receiving the query result returned by the server through the second analyzing unit 43, and analyzing the query result to obtain result information; calculating a second risk value corresponding to the result information by a second calculation unit 44; the risk vector is obtained by the evaluation unit 45 based on the first risk value and the second risk value, and risk evaluation is performed on the risk vector to obtain risk information.
In this embodiment, the information query request of the user side and the query result returned by the server are both parsed, and the risk value is calculated on the input information and the result information obtained by the parsing, so as to obtain a risk vector of the query behavior of the user side, according to the risk vector, the risk degree of the query behavior of the user side can be defined in a digital form, and according to different risk degrees, different solutions are provided, so that the query behavior with risk is prevented in time, the information leakage problem is avoided, the information security is protected, and further the technical problems that in the related technology, when the user side queries the information in the system, an effective risk assessment method is lacking, the information security is low, and information leakage is easy to cause are solved.
Optionally, the first computing unit 42 includes: the first acquisition module is used for acquiring a client risk coefficient corresponding to the client information based on the client information in the input information, wherein the client risk coefficient is a weight value corresponding to the client information input by the client at the visual interface; the first calculation module is used for calculating a product risk coefficient based on the product information in the input information; the second acquisition module is used for extracting inquiry time information in the input information and acquiring a time risk coefficient corresponding to the inquiry time information, wherein the time risk coefficient is a weight value corresponding to the inquiry time information; and the second calculation module is used for calculating a first risk value of the input information based on the client risk coefficient, the product risk coefficient and the moment risk coefficient.
Optionally, the first computing module includes: the first acquisition sub-module is used for acquiring a first product weight value corresponding to the product information based on the product information in the input information; the first query sub-module is used for querying a correlation dictionary based on a user identifier of the user side and a product identifier in the product information to obtain a first product correlation value between the user side and the product information, wherein the correlation dictionary is pre-constructed and used for recording the correlation value among the user side, the client information and the product information, and the correlation value is used for representing the matching degree among the user, the client and the product; the first calculating sub-module is used for calculating a product risk coefficient based on a first product weight value corresponding to the product information and a first product correlation value between the user side and the product information.
Optionally, the first acquisition module includes: the first judging submodule is used for judging the inquiry time in the inquiry time information to obtain a judging result; the second acquisition sub-module is used for acquiring a weight value of business hours as a time risk coefficient corresponding to the query time information when the determination result indicates that the query time is the business hours; and the third acquisition sub-module is used for acquiring the weight value of the non-business time as the time risk coefficient corresponding to the query time information when the determination result indicates that the query time is the non-business time.
Optionally, the second computing unit 44 includes: the third calculation module is used for calculating a risk coefficient of the inquiring client corresponding to the client information in the result information; the fourth calculation module is used for calculating a risk coefficient of the query product corresponding to the product information in the result information; and the fifth calculation module is used for calculating a second risk value corresponding to the result information based on the risk coefficient of the query customer, the risk coefficient of the query product and the moment risk coefficient.
Optionally, the third computing module includes: the fourth acquisition sub-module is used for acquiring a weight value corresponding to the client information based on the client information in the result information; the second query sub-module is used for querying a correlation dictionary based on the user identification of the user side and the client identification in the client information to obtain a correlation value between the user side and the client information; and the second calculation sub-module is used for calculating the query client risk coefficient corresponding to the client information in the result information based on the weight value corresponding to the client information and the correlation value between the user side and the client information.
Optionally, the fourth calculation module includes: a fifth obtaining sub-module, configured to obtain a second product weight value corresponding to the product information based on the product information in the result information; the third query sub-module is used for querying a correlation dictionary based on the user identification of the user side and the product identification in the product information to obtain a second product correlation value between the user side and the product information; and the third calculation sub-module is used for calculating the risk coefficient of the query product corresponding to the product information in the result information based on the first product weight value corresponding to the product information and the second product correlation value between the user side and the product information.
Optionally, the evaluation unit 45 comprises: the first splicing module is used for splicing the first risk value and the second risk value to obtain a risk vector; the first matching module is used for matching the vector interval based on the risk vector to obtain a risk grade corresponding to the risk vector; the first generation module is used for configuring a processing strategy for the information inquiry request and the inquiry result of the user terminal based on the risk level, and generating risk information and returning the risk information to the user terminal.
The risk assessment device for information query may further include a processor and a memory, where the first analysis unit 41, the first calculation unit 42, the second analysis unit 43, the second calculation unit 44, the assessment unit 45, and the like are stored as program units in the memory, and the processor executes the program units stored in the memory to implement corresponding functions.
The processor includes a kernel, and the kernel fetches a corresponding program unit from the memory. The kernel may set one or more kernel parameters to perform risk assessment on the query information.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM), which includes at least one memory chip.
According to another aspect of the embodiment of the present invention, there is also provided a computer readable storage medium, where the computer readable storage medium includes a stored computer program, and when the computer program runs, a risk assessment method for controlling a device in which the computer readable storage medium is located to perform the above information query is provided.
According to another aspect of the embodiments of the present invention, there is also provided an electronic device including one or more processors and a memory for storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the risk assessment method for information query described above.
The present application also provides a computer program product adapted to perform, when executed on a data processing device, a program initialized with the method steps of: receiving an information inquiry request sent by a user side, and analyzing the information inquiry request to obtain input information, wherein the information inquiry request is used for inquiring customer information and product information corresponding to products handled by the customer; calculating a first risk value corresponding to the input information; receiving a query result returned by the server, and analyzing the query result to obtain result information; calculating a second risk value corresponding to the result information; and obtaining a risk vector based on the first risk value and the second risk value, and performing risk assessment on the risk vector to obtain risk information.
Fig. 5 is a block diagram of a hardware structure of an electronic device (or a mobile device) of a risk assessment method of information query according to an embodiment of the present invention. As shown in fig. 5, the electronic device may include one or more (shown in fig. 5 as 502a, 502b, … …,502 n) processors 502 (the processors 502 may include, but are not limited to, a microprocessor MCU, a programmable logic device FPGA, etc. processing means), a memory 504 for storing data. In addition, the method may further include: a display, an input/output interface (I/O interface), a Universal Serial Bus (USB) port (which may be included as one of the ports of the I/O interface), a network interface, a keyboard, a power supply, and/or a camera. It will be appreciated by those of ordinary skill in the art that the configuration shown in fig. 5 is merely illustrative and is not intended to limit the configuration of the electronic device described above. For example, the electronic device may also include more or fewer components than shown in FIG. 5, or have a different configuration than shown in FIG. 5.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
In the foregoing embodiments of the present invention, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed technology content may be implemented in other manners. The above-described embodiments of the apparatus are merely exemplary, and the division of the units, for example, may be a logic function division, and may be implemented in another manner, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention.

Claims (10)

1. A risk assessment method for information query, comprising:
receiving an information inquiry request sent by a user side, and analyzing the information inquiry request to obtain input information, wherein the information inquiry request is used for inquiring customer information and product information corresponding to products handled by a customer;
calculating a first risk value corresponding to the input information;
receiving a query result returned by a server, and analyzing the query result to obtain result information;
calculating a second risk value corresponding to the result information;
and obtaining a risk vector based on the first risk value and the second risk value, and performing risk assessment on the risk vector to obtain risk information.
2. The method of evaluating according to claim 1, wherein the step of calculating the first risk value corresponding to the input information includes:
acquiring a client risk coefficient corresponding to the client information based on the client information in the input information, wherein the client risk coefficient is a weight value corresponding to the client information input by the client on a visual interface;
calculating a product risk coefficient based on the product information in the input information;
Extracting inquiry time information in the input information, and acquiring a time risk coefficient corresponding to the inquiry time information, wherein the time risk coefficient is a weight value corresponding to the inquiry time information;
and calculating a first risk value of the input information based on the customer risk coefficient, the product risk coefficient and the moment risk coefficient.
3. The evaluation method according to claim 2, wherein the step of calculating a product risk factor based on the product information in the input information includes:
acquiring a first product weight value corresponding to the product information based on the product information in the input information;
inquiring a relevance dictionary based on a user identifier of the user side and a product identifier in the product information to obtain a first product relevance value between the user side and the product information, wherein the relevance dictionary is pre-constructed and used for recording the relevance value among the user side, the client information and the product information, and the relevance value is used for representing the matching degree among the user, the client and the product;
and calculating the product risk coefficient based on a first product weight value corresponding to the product information and a first product correlation value between the user side and the product information.
4. The evaluation method according to claim 2, wherein the step of acquiring the time risk coefficient corresponding to the inquiry time information based on the inquiry time information in the input information includes:
judging the inquiry time in the inquiry time information to obtain a judging result;
acquiring a weight value of business hours as a time risk coefficient corresponding to the query time information under the condition that the determination result indicates that the query time is the business hours;
and under the condition that the judging result indicates that the inquiring time is non-business time, acquiring a weight value of the non-business time as a time risk coefficient corresponding to the inquiring time information.
5. The evaluation method according to claim 1, wherein the step of calculating the second risk value corresponding to the result information includes:
calculating a risk coefficient of a query client corresponding to the client information in the result information;
calculating a risk coefficient of the query product corresponding to the product information in the result information;
and calculating a second risk value corresponding to the result information based on the risk coefficient of the query customer, the risk coefficient of the query product and the moment risk coefficient.
6. The method of claim 5, wherein the step of calculating a risk factor of the querying client corresponding to the client information in the result information comprises:
acquiring a weight value corresponding to the client information based on the client information in the result information;
inquiring a relevance dictionary based on the user identification of the user side and the client identification in the client information to obtain a relevance value between the user side and the client information;
and calculating a query client risk coefficient corresponding to the client information in the result information based on the weight value corresponding to the client information and the correlation value between the client and the client information.
7. The method according to claim 5, wherein the step of calculating the risk factor of the query product corresponding to the product information in the result information includes:
acquiring a second product weight value corresponding to the product information based on the product information in the result information;
inquiring a relevance dictionary based on the user identification of the user side and the product identification in the product information to obtain a second product relevance value between the user side and the product information;
and calculating a risk coefficient of the query product corresponding to the product information in the result information based on the first product weight value corresponding to the product information and the second product correlation value between the user side and the product information.
8. The evaluation method according to claim 1, wherein the steps of obtaining a risk vector based on the first risk value and the second risk value, and performing risk evaluation on the risk vector, and obtaining risk information include:
splicing the first risk value and the second risk value to obtain a risk vector;
based on the risk vector matching vector interval, obtaining a risk level corresponding to the risk vector;
and configuring a processing strategy for the information query request and the query result of the user terminal based on the risk grade, generating risk information and returning the risk information to the user terminal.
9. A risk assessment apparatus for information inquiry, comprising:
the first analysis unit is used for receiving an information inquiry request sent by a user side, and analyzing the information inquiry request to obtain input information, wherein the information inquiry request is used for inquiring customer information and product information corresponding to products handled by a customer;
the first calculating unit is used for calculating a first risk value corresponding to the input information;
the second analysis unit is used for receiving the query result returned by the server and analyzing the query result to obtain result information;
The second calculating unit is used for calculating a second risk value corresponding to the result information;
the evaluation unit is used for obtaining a risk vector based on the first risk value and the second risk value, and performing risk evaluation on the risk vector to obtain risk information.
10. An electronic device comprising one or more processors and a memory for storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the risk assessment method of information query of any of claims 1-8.
CN202410108052.4A 2024-01-25 2024-01-25 Risk assessment method and device for information query and electronic equipment Pending CN117874791A (en)

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