User type determination method and device, equipment and storage medium

Abstract

The invention provides a user type determination method and device, equipment and a storage medium, and relates to the technical field of data processing. The method comprises the following steps: determining a plurality of user information types and corresponding user attribute fields according to a user identifier of a target user; obtaining a user attribute corresponding to each user attribute field in a preset database; according to the plurality of user attributes, determining an influence degree among the plurality of user information types, and according to the influence degree among the plurality of user information types, determining a consistency verification result of the plurality of user information types; when the consistency verification result is that the verification is passed, determining an index weight of each user attribute and an influence degree among a plurality of user attributes corresponding to each user information type; and determining the user type of the target user according to the index weight of the user attribute, the influence degree between the user information types and the influence degree between the plurality of user attributes corresponding to the user information types, thereby improving the accuracy.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method, device, equipment and storage medium for determining a user type. Background Art

[0002] In many scenarios, it is necessary to determine the user type. For example, in the subsidy distribution scenario, it is necessary to determine whether the user is eligible to receive the subsidy. In this scenario, the user type includes those who are eligible for the subsidy and those who are not eligible for the subsidy.

[0003] In related technologies, when staff need to determine the user type of a certain user, they can first retrieve relevant information such as the user's age, gender, household registration, health status, etc. from the database, analyze and review the relevant information, and thus determine the user type of the user.

[0004] However, in the above process, the staff needs to analyze and judge a large amount of information, which will consume a lot of manpower and time, and human subjective judgment is prone to errors, resulting in poor accuracy in determining the user type. Summary of the invention

[0005] The present application provides a method, apparatus, device and storage medium for determining a user type, so as to solve the problem of poor accuracy in determining a user type.

[0006] In a first aspect, the present application provides a method for determining a user type, comprising:

[0007] Determine, according to the user identifier of the target user, multiple user information types and user attribute fields corresponding to each user information type;

[0008] Obtain the user attribute corresponding to each user attribute field in the preset database to obtain multiple user attributes;

[0009] Determining the influence degree between the multiple user information types according to the multiple user attributes, and determining the consistency verification result of the multiple user information types according to the influence degree between the multiple user information types;

[0010] When the consistency check result is that the check is passed, determining the indicator weight of each user attribute and the influence degree between the multiple user attributes corresponding to each user information type;

[0011] The user type of the target user is determined according to the index weight of each user attribute, the influence degree between the multiple user information types, and the influence degree between the multiple user attributes corresponding to each user information type.

[0012] In a possible design, determining the user type of the target user according to the index weight of each user attribute, the influence degree between the multiple user information types, and the influence degree between the multiple user attributes corresponding to each user information type includes:

[0013] Normalizing the influence degrees between the multiple user information types to obtain a first feature vector;

[0014] For any user information type, normalize the influence degree between multiple user attributes corresponding to the user information type to obtain a second feature vector corresponding to the user information type;

[0015] The user type of the target user is determined according to the indicator weight of each user attribute, the first feature vector, and the second feature vector corresponding to each user information type.

[0016] In a possible design, determining the user type of the target user according to the indicator weight of each user attribute, the first feature vector, and the second feature vector corresponding to each user information type includes:

[0017] For any user information type, determining an initial type value corresponding to the user information type according to the indicator weights of multiple user attributes corresponding to the user information type and a second feature vector corresponding to the user information type;

[0018] Determine a third eigenvector according to the initial type value corresponding to each user information type;

[0019] A user type of the target user is determined according to the first feature vector and the third feature vector.

[0020] In a possible design, determining an initial type value corresponding to the user information type according to the indicator weights of the plurality of user attributes corresponding to the user information type and the second feature vector corresponding to the user information type includes:

[0021] Determining a weight vector according to the indicator weights of the plurality of user attributes corresponding to the user information type;

[0022] The weight vector and the second feature vector are multiplied to obtain the initial type value.

[0023] In a possible design, determining the user type of the target user according to the first feature vector and the third feature vector includes:

[0024] Determine the product of the first eigenvector and the third eigenvector as a target eigenvalue;

[0025] If the target characteristic value is within a first preset range, determining that the user type is a first type, the first type is used to indicate that the target user has target qualifications;

[0026] If the target characteristic value is within a second preset range, determining that the user type is a second type, the second type is used to indicate that the target user does not have the target qualification;

[0027] If the target characteristic value is within a third preset range, preset indication information is output, where the preset indication information is used to indicate that the user type of the target user cannot be determined.

[0028] In a possible design, determining consistency verification results of the multiple user information types according to the influence degree between the multiple user information types includes:

[0029] Determining the number of types of the plurality of user information types;

[0030] Determine the weight vector corresponding to each user information type;

[0031] The consistency check result is determined according to the influence degree between the multiple user information types, the number of types, and the weight vector corresponding to each user information type.

[0032] In a possible design, determining the consistency check result according to the influence degree between the multiple user information types, the number of the types, and the weight vector corresponding to each user information type includes:

[0033] Determining maximum feature values ​​of the multiple user features according to the influence degree between the multiple user information types, the number of the types, and the weight vector corresponding to each user information type;

[0034] Determining a current consistency index according to the number of types and the maximum eigenvalue;

[0035] Get the preset random consistency indicator;

[0036] Determine the ratio of the current consistency index to the random consistency index as a consistency check coefficient;

[0037] If the consistency check coefficient is less than or equal to a preset threshold, determining that the consistency check result is a check pass;

[0038] If the consistency check coefficient is greater than the preset threshold, the consistency check result is determined to be a check failure.

[0039] In one possible design, the indicator weight of each user attribute is determined, including:

[0040] Determine the preset model;

[0041] The plurality of user attributes are processed by the preset model to obtain an indicator weight of each user attribute.

[0042] In a second aspect, the present application provides a device for determining a user type, comprising: a first determination module, an acquisition module, a second determination module, a third determination module and a fourth determination module, wherein:

[0043] The first determination module is used to determine multiple user information types and user attribute fields corresponding to each user information type according to the user identifier of the target user;

[0044] The acquisition module is used to determine multiple user information types and user attribute fields corresponding to each user information type according to the user identification of the target user;

[0045] The second determination module is used to determine the influence degree between the multiple user information types according to the multiple user attributes, and determine the consistency verification result of the multiple user information types according to the influence degree between the multiple user information types;

[0046] The third determination module is used to determine the indicator weight of each user attribute and the influence degree between multiple user attributes corresponding to each user information type when the consistency check result is a check pass;

[0047] The fourth determination module is used to determine the user type of the target user according to the indicator weight of each user attribute, the influence degree between the multiple user information types, and the influence degree between the multiple user attributes corresponding to each user information type.

[0048] In a possible design, the fourth determining module is specifically configured to:

[0049] Normalizing the influence degrees between the multiple user information types to obtain a first feature vector;

[0050] For any user information type, normalize the influence degree between multiple user attributes corresponding to the user information type to obtain a second feature vector corresponding to the user information type;

[0051] The user type of the target user is determined according to the indicator weight of each user attribute, the first feature vector, and the second feature vector corresponding to each user information type.

[0052] In a possible design, the fourth determining module is specifically configured to:

[0053] For any user information type, determining an initial type value corresponding to the user information type according to the indicator weights of multiple user attributes corresponding to the user information type and a second feature vector corresponding to the user information type;

[0054] Determine a third eigenvector according to the initial type value corresponding to each user information type;

[0055] A user type of the target user is determined according to the first feature vector and the third feature vector.

[0056] In a possible design, the fourth determining module is specifically configured to:

[0057] Determining a weight vector according to the indicator weights of the plurality of user attributes corresponding to the user information type;

[0058] The weight vector and the second feature vector are multiplied to obtain the initial type value.

[0059] In a possible design, the fourth determining module is specifically configured to:

[0060] Determine the product of the first eigenvector and the third eigenvector as a target eigenvalue;

[0061] If the target characteristic value is within a first preset range, determining that the user type is a first type, the first type is used to indicate that the target user has target qualifications;

[0062] If the target characteristic value is within a second preset range, determining that the user type is a second type, the second type is used to indicate that the target user does not have the target qualification;

[0063] If the target characteristic value is within a third preset range, preset indication information is output, where the preset indication information is used to indicate that the user type of the target user cannot be determined.

[0064] In a possible design, the second determining module is specifically used to:

[0065] Determining the number of types of the plurality of user information types;

[0066] Determine the weight vector corresponding to each user information type;

[0067] The consistency check result is determined according to the influence degree between the multiple user information types, the number of types, and the weight vector corresponding to each user information type.

[0068] In a possible design, the second determining module is specifically used to:

[0069] Determining maximum feature values ​​of the multiple user features according to the influence degree between the multiple user information types, the number of the types, and the weight vector corresponding to each user information type;

[0070] Determining a current consistency index according to the number of types and the maximum eigenvalue;

[0071] Get the preset random consistency indicator;

[0072] Determine the ratio of the current consistency index to the random consistency index as a consistency check coefficient;

[0073] If the consistency check coefficient is less than or equal to a preset threshold, determining that the consistency check result is a check pass;

[0074] If the consistency check coefficient is greater than the preset threshold, the consistency check result is determined to be a check failure.

[0075] In a possible design, the third determination module is specifically used to:

[0076] Determine the preset model;

[0077] The plurality of user attributes are processed by the preset model to obtain an indicator weight of each user attribute.

[0078] In a third aspect, an embodiment of the present application provides an electronic device comprising: at least one processor and a memory; the memory stores computer execution instructions; at least one processor executes the computer execution instructions stored in the memory, so that the at least one processor executes the method for determining the user type as described in the first aspect above and various possible designs of the first aspect.

[0079] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer execution instructions are stored. When a processor executes the computer execution instructions, the method for determining the user type as described in the first aspect and various possible designs of the first aspect is implemented.

[0080] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program. When the computer program is executed by a processor, it implements the method for determining the user type as described in the first aspect and various possible designs of the first aspect.

[0081] The user type determination method, apparatus, device and storage medium provided in the present application can determine the user information type and user attributes from a preset database according to the user identifier of the user when the user type needs to be determined. According to the user information type and user attributes, the indicator weight of the user attribute, the influence degree between multiple user information types, and the influence degree between multiple user attributes corresponding to each user information type can be determined, thereby determining the user type of the user. Through the data processing method, the problem of low efficiency and accuracy in judging the user type is solved. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0083] Figure 1 A schematic diagram of the system architecture provided for an embodiment of the present application;

[0084] Figure 2 A flowchart of a method for determining a user type provided in an embodiment of the present application;

[0085] Figure 3 A schematic diagram of another method for determining a user type provided in an embodiment of the present application;

[0086] Figure 4 A schematic diagram of the structure of a device for determining a user type provided in an embodiment of the present application;

[0087] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0088] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0089] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0090] In the technical solution of this application, the collection, storage, use, processing, transmission, provision and disclosure of information such as financial data or user data involved shall comply with the provisions of relevant laws and regulations and shall not violate public order and good morals.

[0091] It should be noted that in the embodiments of the present application, certain software, components, models and other existing solutions in the industry may be mentioned, and they should be regarded as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of the present application, but it does not mean that the applicant has or will necessarily use the solution.

[0092] For ease of understanding, the following Figure 1 , the system architecture applicable to the embodiments of the present application is described.

[0093] Figure 1 This is a schematic diagram of the system architecture provided by the embodiment of this application. Figure 1 , including a terminal device 101 and a storage device 102. The terminal device 101 may be a local device of the user, and a software tool may be set in the terminal device 101, and the staff can query the user type through the software tool, for example, whether the user is eligible for subsidy. The storage device 102 may have a preset database for storing the user's personal information. The storage device 102 may be a USB flash drive, a hard disk, or a cloud storage device, etc.

[0094] In related technologies, when a staff member needs to determine the user type of a certain user, he or she can first retrieve the user's age, gender, household registration, health status and other related information from the database, analyze and review the relevant information, and thus determine the user type of the user. However, in the above process, the staff member needs to analyze and judge a large amount of information, which will consume a lot of manpower and time, and human subjective judgment is prone to errors, resulting in poor accuracy.

[0095] In response to the above technical problems, in an embodiment of the present application, when it is necessary to determine the user type, the user information type and user attributes can be determined from a preset database based on the user's user identifier. Based on the user information type and user attributes, the indicator weight of the user attribute, the degree of influence between multiple user information types, and the degree of influence between multiple user attributes corresponding to each user information type can be determined, thereby determining the user type of the user. Through data processing methods, the efficiency and accuracy of judging the user type are improved.

[0096] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0097] Figure 2 A flowchart of a method for determining a user type provided in an embodiment of the present application. Figure 2 As shown, the method may include the following steps:

[0098] S201. Determine, according to a user identifier of a target user, multiple user information types and a user attribute field corresponding to each user information type.

[0099] The execution subject of the embodiment of the present application may be a terminal device, or a user type determination device set in the terminal device. The user type determination device may be implemented by software, or by a combination of software and hardware.

[0100] The user identification of the target user may be used to indicate a unique target user. For example, the user identification of the target user may refer to the ID number of the target user. The target user may refer to an elderly person who needs to be reviewed for eligibility to receive the subsidy.

[0101] The user information type can be used to describe different types of personal information, and multiple user information types corresponding to the target user can be determined according to the user identifier. For example, the user information type may include the target user's personal information, family environment, and assistance policies in the area where the target user is located.

[0102] The user attribute field can be used to specifically describe different user information types, and a user information type can correspond to multiple user attribute fields. For example, when the user information type is the personal information of the target user, the corresponding user attribute fields may include age, health status, household registration, etc.; when the user information type is the family environment of the target user, the corresponding user attribute fields may include annual income, disposable income, etc.

[0103] S202: Obtain a user attribute corresponding to each user attribute field in a preset database to obtain a plurality of user attributes.

[0104] The preset database may be located in a storage device, and the storage device may be a USB flash drive, a solid state drive, or a cloud storage device.

[0105] User attributes may refer to values ​​corresponding to user attribute fields, and corresponding user attributes may be found in a preset database according to the user attribute fields. For example, when the user information type is the personal information of the target user, the corresponding user attribute fields may include age, household registration, etc., and multiple user attributes such as "age is 60 years old" and "household registration is Beijing" may be obtained from the preset database.

[0106] S203: Determine the influence degree between the multiple user information types according to the multiple user attributes, and determine the consistency verification result of the multiple user information types according to the influence degree between the multiple user information types.

[0107] The degree of influence refers to the relative importance of one factor over another. User attributes can affect the user information type, thereby affecting the importance of the user information types. For example, assuming that the user attribute corresponding to user information type 1 of an elderly person is poor health, and the user attribute corresponding to user information type 2 is age 60, then based on user information type 1, it can be determined that the elderly person may have a difficult life due to poor health, that is, user information type 1 is more important than user information type 2.

[0108] The influence degree between multiple user information types may be determined in the following manner: the influence degree between every two user information types may be determined first, until the influence degrees between all user information types are determined, and then the influence degrees between multiple user information types may be represented in a matrix.

[0109] The degree of influence between two user information types can be determined as follows:

[0110] The degree of influence between two user information types can be determined by proportional scaling. Assuming that there are two user information types, user information type 1 and user information type 2, the degree of influence between user information type 1 and user information type 2 can be determined according to a proportional scaling table. The proportional scaling table is shown in Table 1.

[0111] Table 1

[0112] User information type 1 is better than user information type 2 Impact Equally important 1 Slightly important 3 Strong and important 5 Strongly important 7 Extremely important 9 The middle value of two adjacent judgments 2，4，6，8

[0113] Please refer to Table 1. When user information type 1 is equally important as user information type 2, the impact level is 1; when user information type 1 is slightly more important than user information type 2, the impact level is 3; when user information type 1 is more strongly important than user information type 2, the impact level is 5; when user information type 1 is strongly more important than user information type 2, the impact level is 7; when user information type 1 is extremely more important than user information type 2, the impact level is 9; when the impact level between user information type 1 and user information type 2 is between slightly important and equally important, the impact level is 2; when the impact level between user information type 1 and user information type 2 is between strongly important and slightly important, the impact level is 4; when the impact level between user information type 1 and user information type 2 is between strongly important and strongly important, the impact level is 6; when the impact level between user information type 1 and user information type 2 is between extremely important and strongly important, the impact level is 8.

[0114] Optionally, the degree of influence of user information type 2 compared to user information type 1 is the inverse of the degree of influence of user information type 1 compared to user information type 2. For example, when the degree of influence of user information type 1 compared to user information type 2 is 3, the degree of influence of user information type 2 compared to user information type 1 is 1 / 3.

[0115] For example, assuming that the target user includes three user information types, namely user information type 1, user information type 2, and user information type 3, the influence degrees between the three user information types may be as shown in Table 2.

[0116] Table 2

[0117]

[0118]

[0119] Please refer to Table 2. When the influence degree between user type 2 and user information type 1 is between strongly important and relatively important, the influence degree is 6; the influence degree between user type 1 and user information type 2 is 1 / 6; when user information type 3 is slightly important compared to user information type 1, the influence degree is 3; the influence degree between user type 1 and user information type 3 is 1 / 3; when the influence degree between user information type 2 and user information type 3 is between relatively important and slightly important, the influence degree is 4; the influence degree between user type 3 and user information type 2 is 1 / 4.

[0120] According to the influence degree between multiple user information types, the consistency check results of multiple user information types can be determined, and the consistency check results can be used to measure the size of the influence degree deviation. Among them, the consistency check result can include verification pass and verification fail, that is, when the influence degree deviation between multiple user information types is large, the consistency check result is verification fail; when the influence degree deviation between multiple user information types is small, the consistency check result is verification pass.

[0121] S204: When the consistency check result is passed, determine the index weight of each user attribute and the influence degree between multiple user attributes corresponding to each user information type.

[0122] The indicator weight of user attributes refers to the degree of influence of user attributes on user information types. The value of indicator weight can be 0, 1, 2, 3, 4, 5, etc. The larger the indicator weight, the greater the influence of user attributes on user information types. That is, when the indicator weight is 0, it means that user attributes have no influence on user information types; when the indicator weight is 5, it means that user attributes have a great influence on user information types.

[0123] By processing the user attributes through the preset model, the indicator weight of each user attribute can be obtained. The preset model can be a model pre-trained by the user, which is used to determine the indicator weight of the user attribute.

[0124] The influence degree between multiple user attributes may be determined in the following manner: the influence degree between every two user attributes may be determined first, until the influence degrees between all user attributes are determined, and then the influence degrees between multiple user attributes may be represented in a matrix manner.

[0125] The degree of influence between two user attributes can be determined as follows:

[0126] The influence degree between two user attributes can be determined by a proportional scale. Assuming that the user information type has two user attributes, user attribute 1 and user attribute 2, the influence degree between user attribute 1 and user attribute 2 can be determined according to a proportional scale table. The proportional scale table is shown in Table 3.

[0127] Table 3

[0128] User attribute 1 is better than user attribute 2 Impact Equally important 1 Slightly important 3 Strong and important 5 Strongly important 7 Extremely important 9 The middle value of two adjacent judgments 2，4，6，8

[0129] Please refer to Table 3. When user attribute 1 is equally important to user attribute 2, the influence degree is 1; when user attribute 1 is slightly more important than user attribute 2, the influence degree is 3; when user attribute 1 is more important than user attribute 2, the influence degree is 5; when user attribute 1 is strongly important than user attribute 2, the influence degree is 7; when user attribute 1 is extremely important than user attribute 2, the influence degree is 9; when the influence degree between user attribute 1 and user attribute 2 is between slightly important and equally important, the influence degree is 2; when the influence degree between user attribute 1 and user attribute 2 is between strongly important and slightly important, the influence degree is 4; when the influence degree between user attribute 1 and user attribute 2 is between strongly important and strongly important, the influence degree is 6; when the influence degree between user attribute 1 and user attribute 2 is between extremely important and strongly important, the influence degree is 8.

[0130] Optionally, the influence degree of user attribute 2 compared to user attribute 1 is the inverse of the influence degree of user attribute 1 compared to user attribute 2. For example, when the influence degree of user attribute 1 compared to user attribute 2 is 3, the influence degree of user attribute 2 compared to user attribute 1 is 1 / 3.

[0131] S205: Determine the user type of the target user according to the indicator weight of each user attribute, the influence degree between multiple user information types, and the influence degree between multiple user attributes corresponding to each user information type.

[0132] The terminal device can obtain the index weight of each user attribute, the influence degree between multiple user information types, and the influence degree between multiple user attributes corresponding to each user information type, and perform aggregation operations on them to determine the user type of the target user.

[0133] The user type of the target user may be used to indicate whether the target user has target qualifications, wherein the target qualifications may refer to qualifications for receiving subsidies.

[0134] In an embodiment of the present application, when it is necessary to determine the user type of the target user, multiple user information types and corresponding user attribute fields of the target user can be determined first according to the user identifier of the target user, and the corresponding user attributes can be obtained from a preset database according to the user attribute fields; according to the multiple user attributes, the degree of influence between the multiple user information types can be determined by means of a proportional scale; according to the degree of influence between the multiple user information types, a consistency check result can be determined, and when the consistency check result is a passed check, the indicator weight of each user attribute is determined, and the degree of influence between the multiple user attributes corresponding to each user information type is determined by means of a proportional scale; by performing aggregation operations on the indicator weight of each user attribute, the degree of influence between the multiple user information types, and the degree of influence between the multiple user attributes corresponding to each user information type, the user type of the target user can be determined, and the efficiency and accuracy of user type determination are improved through data processing.

[0135] Based on any of the above embodiments, Figure 3 , a method for determining the user type is described in detail.

[0136] Figure 3 This is a process diagram of another method for determining user types provided in an embodiment of the present application. Figure 3 , the method may include:

[0137] S301. Determine, according to a user identifier of a target user, multiple user information types and a user attribute field corresponding to each user information type.

[0138] S302: Obtain a user attribute corresponding to each user attribute field in a preset database to obtain a plurality of user attributes.

[0139] S303: Determine the influence degree between multiple user information types according to multiple user attributes.

[0140] It should be noted that the execution process of the above S301-S303 can refer to S201-S203, which will not be repeated here.

[0141] S304: Determine the number of the multiple user information types and a weight vector corresponding to each user information type according to the influence degree between the multiple user information types.

[0142] The number of user information types can be determined according to the target user identifier, and this number is the number of types of user information types.

[0143] According to the indicator weights of the multiple user attributes corresponding to the user information type, the weight vector can be determined. For example, assuming that the indicator weights of the multiple user attributes corresponding to the user information type are as shown in Table 4.

[0144] Table 4

[0145] Indicator weight User Attribute 1 User Attribute 2 User Attribute 3 User Attribute 4 User Attribute 5 User Attribute 6 User information type 1 2 3 0 0 0 0 User information type 2 0 0 1 3 0 0 User Information Type 3 0 0 0 0 1 4

[0146] Please refer to Table 4, the weight vector corresponding to user information type 1 is [2,3], the weight vector corresponding to user information type 2 is [1,3], and the weight vector corresponding to user information type 3 is [1,4].

[0147] S305: Determine a consistency check coefficient according to the influence degree between the multiple user information types, the number of types, and the weight vector corresponding to each user information type.

[0148] According to the influence degree between the multiple user information types, the number of types, and the weight vector corresponding to each user information type, the maximum feature value of the multiple user features is determined.

[0149] For example, suppose A is the influence matrix between user information types, W is the weight vector, and W i is the i-th component of W, n is the number of types, (AW) i is the i-th component of AW, then the maximum eigenvalue can be determined to be λ max for:

[0150]

[0151] Based on the number of types and the maximum eigenvalue, the current consistency index can be determined.

[0152] For example, assuming n is the number of types, λ max is the maximum eigenvalue, then the current consistency index CI can be determined as:

[0153]

[0154] According to the number of types, a preset random consistency index is obtained. The corresponding random consistency index can be determined through Table 5.

[0155] Table 5

[0156] n 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 RI 0 0 0.52 0.89 1.12 1.26 1.36 1.41 1.46 1.49 1.52 1.54 1.56 1.58 1.59

[0157] Please see Table 5. For example, when the number of types n is 3, the random consistency index RI is 0.52.

[0158] The ratio of the current consistency index to the random consistency index is determined as the consistency check coefficient.

[0159] For example, assuming that CI is the current consistency index and RI is the random consistency index, the consistency check coefficient CR can be determined as:

[0160]

[0161] S306: Determine whether the consistency check coefficient is less than or equal to a preset threshold.

[0162] The preset threshold value may be a value preset by a user in the terminal device. For example, the preset threshold value may be 0.1.

[0163] If so, it is determined that the consistency check result is passed, and S307 is executed.

[0164] If not, it is determined that the consistency check result is a check failure, and S316 is executed.

[0165] S307: Determine the index weight of each user attribute and the influence degree between multiple user attributes corresponding to each user information type.

[0166] It should be noted that the execution process of the above S307 can refer to S204, which will not be described in detail here.

[0167] S308: Normalize the influence degrees among the multiple user information types to obtain a first feature vector.

[0168] The first eigenvector can be determined in the following manner: normalizing the matrix of the degree of influence between multiple user information types by columns, that is, dividing the value of the degree of influence of the user information type by the sum of the values ​​of the column where the user information type is located, to obtain a normalized matrix after normalization; summing the normalized matrix by rows to obtain a normalized vector, and dividing each element of the normalized vector by the number of types to obtain the first eigenvector.

[0169] For example, assuming that the influence degree between user information types is as shown in Table 2, see Table 2, then the matrix of the influence degree between user information types is normalized by column, and a normalized matrix can be obtained as shown in Table 6.

[0170] Table 6

[0171] Impact User information type 1 User information type 2 User Information Type 3 User information type 1 0.67 0.55 0.70 User information type 2 0.11 0.09 0.06 User Information Type 3 0.22 0.36 0.24

[0172] Please refer to Table 6. After summing the normalized matrix row by row, we get the normalized vector [1.92, 0.26, 0.82]. By dividing each element of the normalized vector by the number of types, we get the first eigenvector [0.64, 0.09, 0.27].

[0173] S309: For any user information type, normalize the influence degrees between multiple user attributes corresponding to the user information type to obtain a second feature vector corresponding to the user information type.

[0174] The second eigenvector can be determined in the following manner: the matrix of the degree of influence between multiple user attributes is normalized by column, that is, the value of the degree of influence of the user attribute is divided by the sum of the values ​​of the column where the user attribute is located, and a normalized matrix after normalization can be obtained; the normalized matrix is ​​summed by row to obtain a normalized vector, and each element of the normalized vector is divided by the number of types to obtain the second eigenvector.

[0175] For example, assuming that the user attributes corresponding to any user information type include user attribute 1 and user attribute 2, the influence degree between user attribute 1 and user attribute 2 is shown in Table 7.

[0176] Table 7

[0177] Impact User Attribute 1 User Attribute 2 User Attribute 1 1 3 User Attribute 2 1 / 3 1

[0178] Please refer to Table 7. The matrix of the influence degree between user attribute 1 and user attribute 2 is normalized by column, and a normalized matrix as shown in Table 8 can be obtained.

[0179] Table 8

[0180] Impact User Attribute 1 User Attribute 2 User Attribute 1 0.75 0.75 User Attribute 2 0.25 0.25

[0181] Please refer to Table 8. After summing the normalized matrix row by row, we get the normalized vector [1.5, 0.5]. By dividing each element of the normalized vector by the number of types, we get the second eigenvector [0.75, 0.25].

[0182] S310: Determine an initial type value corresponding to the user information type according to a weight vector corresponding to the user information type and a second feature vector corresponding to the user information type.

[0183] Multiply the weight vector and the second eigenvector to get the initial type value.

[0184] For example, assuming that the weight vector corresponding to the user information type is [2,3], and the second eigenvector corresponding to the user information type is [0.75,0.25], then the initial type value Y corresponding to the user information type is:

[0185] Y = 2 × 0.75 + 3 × 0.25 = 2.25

[0186] S311. Determine a third eigenvector according to an initial type value corresponding to each user information type.

[0187] The third eigenvector is a vector composed of initial type values ​​corresponding to each user information type.

[0188] For example, assuming that the user information types corresponding to the target user include user information type 1, user information type 2, and user information type 3, the initial type value corresponding to user information type 1 is 2.25, the initial type value corresponding to user information type 2 is 1.4, and the initial type value corresponding to user information type 3 is 1.99, then the third eigenvector is [2.25, 1.4, 1.99].

[0189] S312: Determine a target eigenvalue according to the first eigenvector and the third eigenvector.

[0190] The product of the first eigenvector and the third eigenvector is determined as the target eigenvalue.

[0191] For example, assuming that the first eigenvector is [0.64, 0.09, 0.27] and the third eigenvector is [2.25, 1.4, 2.99], the target eigenvalue S is:

[0192] S=0.64×2.25+0.09×1.4+0.27×1.99=2.1

[0193] S313: Determine the user type of the target user according to the range of the target feature value.

[0194] The user types of target users can be divided into three categories, namely type one, type two and type three.

[0195] The user type of the target user can be determined in the following ways:

[0196] When the target characteristic value is within the first preset range, it can be determined that the user type of the target user is the first type;

[0197] When the target characteristic value is within the second preset range, it can be determined that the user type of the target user is the second type, wherein the minimum value of the second preset range is equal to the maximum value of the first preset range;

[0198] When the target characteristic value is within a third preset range, it can be determined that the user type of the target user is a third type, wherein the minimum value of the third preset range is equal to the maximum value of the second preset range.

[0199] If the user type of the target user is the first type, the first type is used to indicate that the target user has the target qualification.

[0200] If the user type of the target user is the second type, the second type is used to indicate that the target user does not have the target qualification.

[0201] If the user type of the target user is the third type, execute S314.

[0202] S314: Output preset indication information.

[0203] The preset indication information is used to indicate that the user type of the target user cannot be determined, that is, the target type of the target user cannot be determined based on existing information and requires manual assistance to determine.

[0204] In an embodiment of the present application, when it is necessary to determine the user type of a target user, multiple user information types and multiple user attributes can be determined based on the user identifier of the target user; based on the multiple user attributes, the degree of influence between the multiple user information types can be determined; based on the degree of influence between the multiple user information types, the number of types, and the weight vector corresponding to each user information type, a consistency check result can be determined, and the consistency check result is used to determine whether the check passes; if the consistency check result is that the check passes, the indicator weight of each user attribute and the degree of influence between the multiple user attributes corresponding to each user information type can be determined; based on the degree of influence between the user information types, the degree of influence between the multiple user attributes corresponding to the user information type, and the weight vector corresponding to the user information type, the target feature value can be determined; based on the target feature value, the user type of the target user can be determined, and the efficiency and accuracy of user type determination can be improved through data processing.

[0205] Figure 4 This is a schematic diagram of the structure of a device for determining a user type provided in an embodiment of the present application. Figure 4 The user type determination device 10 includes: a first determination module 11, an acquisition module 12, a second determination module 13, a third determination module 14 and a fourth determination module 15, wherein:

[0206] The first determination module 11 is used to determine multiple user information types and user attribute fields corresponding to each user information type according to the user identification of the target user;

[0207] The acquisition module 12 is used to determine multiple user information types and user attribute fields corresponding to each user information type according to the user identification of the target user;

[0208] The second determination module 13 is used to determine the influence degree between the multiple user information types according to the multiple user attributes, and determine the consistency verification result of the multiple user information types according to the influence degree between the multiple user information types;

[0209] The third determination module 14 is used to determine the indicator weight of each user attribute and the influence degree between multiple user attributes corresponding to each user information type when the consistency check result is passed;

[0210] The fourth determination module 15 is used to determine the user type of the target user according to the indicator weight of each user attribute, the influence degree between the multiple user information types, and the influence degree between the multiple user attributes corresponding to each user information type.

[0211] A user type determination device provided in an embodiment of the present application can execute the technical solution shown in the above method embodiment, and its implementation principle and beneficial effects are similar, which will not be repeated here.

[0212] In a possible design, the fourth determining module 15 is specifically configured to:

[0213] Normalizing the influence degrees between the multiple user information types to obtain a first feature vector;

[0214] For any user information type, normalize the influence degree between multiple user attributes corresponding to the user information type to obtain a second feature vector corresponding to the user information type;

[0215] The user type of the target user is determined according to the indicator weight of each user attribute, the first feature vector, and the second feature vector corresponding to each user information type.

[0216] In a possible design, the fourth determining module 15 is specifically configured to:

[0217] For any user information type, determining an initial type value corresponding to the user information type according to the indicator weights of multiple user attributes corresponding to the user information type and a second feature vector corresponding to the user information type;

[0218] Determine a third eigenvector according to the initial type value corresponding to each user information type;

[0219] A user type of the target user is determined according to the first feature vector and the third feature vector.

[0220] In a possible design, the fourth determining module 15 is specifically configured to:

[0221] Determining a weight vector according to the indicator weights of the plurality of user attributes corresponding to the user information type;

[0222] The weight vector and the second feature vector are multiplied to obtain the initial type value.

[0223] In a possible design, the fourth determining module 15 is specifically configured to:

[0224] Determine the product of the first eigenvector and the third eigenvector as a target eigenvalue;

[0225] If the target characteristic value is within a first preset range, determining that the user type is a first type, the first type is used to indicate that the target user has target qualifications;

[0226] If the target characteristic value is within a second preset range, determining that the user type is a second type, the second type is used to indicate that the target user does not have the target qualification;

[0227] If the target characteristic value is within a third preset range, preset indication information is output, where the preset indication information is used to indicate that the user type of the target user cannot be determined.

[0228] In a possible design, the second determining module 13 is specifically configured to:

[0229] Determining the number of types of the plurality of user information types;

[0230] Determine the weight vector corresponding to each user information type;

[0231] The consistency check result is determined according to the influence degree between the multiple user information types, the number of types, and the weight vector corresponding to each user information type.

[0232] In a possible design, the second determining module 13 is specifically configured to:

[0233] Determining maximum feature values ​​of the multiple user features according to the influence degree between the multiple user information types, the number of the types, and the weight vector corresponding to each user information type;

[0234] Determining a current consistency index according to the number of types and the maximum eigenvalue;

[0235] Get the preset random consistency indicator;

[0236] Determine the ratio of the current consistency index to the random consistency index as a consistency check coefficient;

[0237] If the consistency check coefficient is less than or equal to a preset threshold, determining that the consistency check result is a check pass;

[0238] If the consistency check coefficient is greater than the preset threshold, the consistency check result is determined to be a check failure.

[0239] In a possible design, the third determining module 14 is specifically configured to:

[0240] Determine the preset model;

[0241] The plurality of user attributes are processed by the preset model to obtain an indicator weight of each user attribute.

[0242] A user type determination device provided in an embodiment of the present application can execute the technical solution shown in the above method embodiment, and its implementation principle and beneficial effects are similar, which will not be repeated here.

[0243] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 5 As shown, the electronic device may include: a transceiver 121 , a processor 122 , and a memory 123 .

[0244] The processor 122 executes the computer execution instructions stored in the memory, so that the processor 122 executes the scheme in the above embodiment. The processor 122 can be a general-purpose processor, including a central processing unit CPU, a network processor (network processor, NP), etc.; it can also be a digital signal processor DSP, an application-specific integrated circuit ASIC, a field programmable gate array FPGA or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components.

[0245] The memory 123 is connected to the processor 122 via a system bus and completes communication between them. The memory 123 is used to store computer program instructions.

[0246] The transceiver 121 may be used to obtain tasks to be executed and configuration information of the tasks to be executed.

[0247] The system bus can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The system bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus. The transceiver is used to realize the communication between the database access device and other computers (such as clients, read-write libraries, and read-only libraries). The memory may include random access memory (RAM) and may also include non-volatile memory.

[0248] The electronic device provided in the embodiment of the present application may be the terminal device of the above embodiment.

[0249] An embodiment of the present application also provides a chip for running instructions, which is used to execute the technical solution of the method for determining the user type in the above embodiment.

[0250] An embodiment of the present application further provides a computer-readable storage medium, in which computer instructions are stored. When the computer instructions are executed on a computer, the computer executes the technical solution of the method for determining the user type in the above embodiment.

[0251] An embodiment of the present application also provides a computer program product, which includes a computer program stored in a computer-readable storage medium. At least one processor can read the computer program from the computer-readable storage medium. When at least one processor executes the computer program, it can implement the technical solution of the method for determining the user type in the above embodiment.

[0252] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of modules is only a logical function division, and there may be other division methods in actual implementation, such as multiple modules can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or modules, which can be electrical, mechanical or other forms.

[0253] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to implement the solution of this embodiment.

[0254] In addition, each functional module in each embodiment of the present application can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit. The above-mentioned module-composed unit can be implemented in the form of hardware or in the form of hardware plus software functional units.

[0255] The above-mentioned integrated module implemented in the form of a software function module can be stored in a computer-readable storage medium. The above-mentioned software function module is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to perform some steps of the methods of various embodiments of the present application.

[0256] It should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the invention may be directly implemented as being executed by a hardware processor, or may be implemented by a combination of hardware and software modules in the processor.

[0257] The memory may include a high-speed RAM memory, and may also include a non-volatile storage NVM, such as at least one disk memory, and may also be a USB flash drive, a mobile hard disk, a read-only memory, a magnetic disk or an optical disk, etc.

[0258] The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of the present application is not limited to only one bus or one type of bus.

[0259] The above storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The storage medium can be any available medium that can be accessed by a general or special purpose computer.

[0260] An exemplary storage medium is coupled to a processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an application specific integrated circuit (ASIC). Of course, the processor and the storage medium can also exist as discrete components in an electronic control unit or a main control device.

[0261] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk and other media that can store program codes.

[0262] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for determining a user type, characterized in that: include: Determine, according to the user identifier of the target user, multiple user information types and user attribute fields corresponding to each user information type; Obtain the user attribute corresponding to each user attribute field in the preset database to obtain multiple user attributes; Determining the influence degree between the multiple user information types according to the multiple user attributes, and determining the consistency verification result of the multiple user information types according to the influence degree between the multiple user information types; When the consistency check result is that the check is passed, determining the indicator weight of each user attribute and the influence degree between the multiple user attributes corresponding to each user information type; The user type of the target user is determined according to the index weight of each user attribute, the influence degree between the multiple user information types, and the influence degree between the multiple user attributes corresponding to each user information type.

2. The method according to claim 1, characterized in that Determining the user type of the target user according to the index weight of each user attribute, the influence degree between the multiple user information types, and the influence degree between the multiple user attributes corresponding to each user information type, includes: Normalizing the influence degrees between the multiple user information types to obtain a first feature vector; For any user information type, normalize the influence degree between multiple user attributes corresponding to the user information type to obtain a second feature vector corresponding to the user information type; The user type of the target user is determined according to the indicator weight of each user attribute, the first feature vector, and the second feature vector corresponding to each user information type.

3. The method according to claim 2, characterized in that Determining the user type of the target user according to the indicator weight of each user attribute, the first feature vector, and the second feature vector corresponding to each user information type includes: For any user information type, determining an initial type value corresponding to the user information type according to the indicator weights of multiple user attributes corresponding to the user information type and a second feature vector corresponding to the user information type; Determine a third eigenvector according to the initial type value corresponding to each user information type; A user type of the target user is determined according to the first feature vector and the third feature vector.

4. The method according to claim 3, characterized in that Determining an initial type value corresponding to the user information type according to the indicator weights of the plurality of user attributes corresponding to the user information type and the second feature vector corresponding to the user information type includes: Determining a weight vector according to the indicator weights of the plurality of user attributes corresponding to the user information type; The weight vector and the second feature vector are multiplied to obtain the initial type value.

5. The method according to claim 3 or 4, characterized in that: Determining the user type of the target user according to the first feature vector and the third feature vector includes: Determine the product of the first eigenvector and the third eigenvector as a target eigenvalue; If the target characteristic value is within a first preset range, determining that the user type is a first type, the first type is used to indicate that the target user has target qualifications; If the target characteristic value is within a second preset range, determining that the user type is a second type, the second type is used to indicate that the target user does not have the target qualification; If the target characteristic value is within a third preset range, preset indication information is output, where the preset indication information is used to indicate that the user type of the target user cannot be determined.

6. The method according to any one of claims 1 to 5, characterized in that: Determining consistency verification results of the multiple user information types according to the influence degree between the multiple user information types includes: Determining the number of types of the plurality of user information types; Determine the weight vector corresponding to each user information type; The consistency check result is determined according to the influence degree between the multiple user information types, the number of types, and the weight vector corresponding to each user information type.

7. The method according to claim 6, characterized in that Determining the consistency check result according to the influence degree between the multiple user information types, the number of the types, and the weight vector corresponding to each user information type includes: Determining maximum feature values ​​of the multiple user features according to the influence degree between the multiple user information types, the number of the types, and the weight vector corresponding to each user information type; Determining a current consistency index according to the number of types and the maximum eigenvalue; Get the preset random consistency indicator; Determine the ratio of the current consistency index to the random consistency index as a consistency check coefficient; If the consistency check coefficient is less than or equal to a preset threshold, determining that the consistency check result is a check pass; If the consistency check coefficient is greater than the preset threshold, the consistency check result is determined to be a check failure.

8. The method according to any one of claims 1 to 7, characterized in that: Determine the indicator weight for each user attribute, including: Determine the preset model; The plurality of user attributes are processed by the preset model to obtain an indicator weight of each user attribute.

9. A device for determining a user type, characterized in that: It includes a first determination module, an acquisition module, a second determination module, a third determination module and a fourth determination module, wherein: The first determination module is used to determine multiple user information types and user attribute fields corresponding to each user information type according to the user identifier of the target user; The acquisition module is used to determine multiple user information types and user attribute fields corresponding to each user information type according to the user identification of the target user; The second determination module is used to determine the influence degree between the multiple user information types according to the multiple user attributes, and determine the consistency verification result of the multiple user information types according to the influence degree between the multiple user information types; The third determination module is used to determine the indicator weight of each user attribute and the influence degree between multiple user attributes corresponding to each user information type when the consistency check result is a check pass; The fourth determination module is used to determine the user type of the target user according to the indicator weight of each user attribute, the influence degree between the multiple user information types, and the influence degree between the multiple user attributes corresponding to each user information type.

10. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 8 when executed by a processor.

12. A computer program product, characterized in that The invention comprises a computer program, which implements the method according to any one of claims 1 to 8 when being executed by a processor.

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