Target user identification method and apparatus, electronic device, and storage medium

Abstract

The application discloses a target user identification method and device, electronic equipment and storage medium. The method comprises: obtaining at least one set of first data of a first user in each interaction stage of at least one interaction stage; the first data represents behavior data of the first user in the corresponding interaction stage; each interaction stage in the at least one interaction stage corresponds to one business stage in the business cycle of the set object; processing the obtained first data based on at least one set dimension to obtain the first score of the first user; wherein the first score is used to determine whether the first user is a target user of the set object; the at least one set dimension includes a dimension representing the active degree of interaction, a dimension representing the influence degree of interaction and / or a dimension representing the ability of the set object.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and particularly relates to a target user identification method and device, electronic equipment and a storage medium. BACKGROUND

[0002] In the related art, the behavior of a user is analyzed by using different dimensions, and a target user is identified from multiple users according to the analysis result. However, the method for analyzing the behavior of a user provided by the related art is not applicable to a car scenario, which leads to an inability to accurately identify a target user in the car scenario. SUMMARY

[0003] Therefore, the embodiments of the present application provide a target user identification method and device, electronic equipment and a storage medium to at least solve the problem that a target user in a car scenario cannot be accurately identified in the related art.

[0004] The technical scheme of the embodiments of the present application is implemented as follows:

[0005] The embodiments of the present application provide a target user identification method, which comprises the following steps:

[0006] obtaining at least one set of first data of a first user in each interaction stage of at least one interaction stage; the first data represents behavior data of the first user in the corresponding interaction stage; each interaction stage of the at least one interaction stage corresponds to one business stage in a business cycle of a set object;

[0007] processing the obtained first data based on at least one set dimension to obtain a first score of the first user; wherein,

[0008] the first score is used to determine whether the first user is a target user of the set object; the at least one set dimension comprises a dimension representing the active degree of interaction, a dimension representing the influence degree of interaction and / or a dimension representing the ability to dominate the set object.

[0009] The embodiments of the present application also provide a target user identification device, which comprises:

[0010] an obtaining unit, configured to obtain at least one set of first data of a first user in each interaction stage of at least one interaction stage; the first data represents behavior data of the first user in the corresponding interaction stage; each interaction stage of the at least one interaction stage corresponds to one business stage in a business cycle of a set object;

[0011] a determining unit, configured to process the obtained first data based on at least one set dimension to obtain a first score of the first user; wherein,

[0012] The first score is used to determine whether the first user is a target user of the setting object; the at least one setting dimension includes a dimension representing an active degree of interaction, a dimension representing an influence degree of interaction, and / or a dimension representing a capability of dominating the setting object.

[0013] The embodiment of the present application further provides an electronic device, comprising a processor and a memory for storing a computer program capable of running on the processor,

[0014] The processor is used to run the computer program, and execute the steps of any method.

[0015] The embodiment of the present application further provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the steps of any method.

[0016] In the embodiment of the present application, at least one set of first data of the first user in each interaction stage of at least one interaction stage is acquired, the acquired first data is processed based on at least one setting dimension, and a first score of the first user is obtained, wherein the first score is used to determine whether the first user is a target user of a setting object, and the at least one setting dimension includes a dimension representing an active degree of interaction, a dimension representing an influence degree of interaction, and / or a dimension representing a capability of dominating the setting object. The behavior data of the user can be analyzed from three dimensions, and then the target user is obtained through the score screening of the user, so that the identification efficiency of the target user is improved. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 An implementation flowchart of a target user identification method provided by an embodiment of the present application is shown;

[0018] Figure 2 Interaction content corresponding to the user in different interaction stages in a car scene provided by an embodiment of the present application;

[0019] Figure 3 An implementation flowchart of a target user identification method provided by an embodiment of the present application is shown;

[0020] Figure 4 An implementation flowchart of a target user identification method provided by another embodiment of the present application is shown;

[0021] Figure 5 An implementation flowchart of a target user identification method provided by another embodiment of the present application is shown;

[0022] Figure 6 An implementation flowchart of a target user identification method provided by another embodiment of the present application is shown;

[0023] Figure 7 A structural diagram of target user identification provided by an embodiment of the present application is shown in the following table.

[0024] Figure 8 A hardware component structural diagram of an electronic device of an embodiment of the present application is shown in the following table. DETAILED DESCRIPTION

[0025] The present application will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0026] In the following description, for the purpose of explanation and not limitation, specific details are set forth, such as particular system configurations, techniques, etc., in order to provide a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application can be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices and methods are omitted so as not to obscure the description of the present application with unnecessary detail.

[0027] It should be noted that the technical solutions described in the embodiments of the present application can be combined arbitrarily without conflict.

[0028] In addition, in the examples of the present application, "first", "second", etc. are used to distinguish similar objects, and do not necessarily describe a specific order or sequence.

[0029] The term "and / or" herein is merely a description of the association relationship between the associated objects, which means that there can be three relationships, for example, A and / or B can mean that A exists alone, A and B exist together, and B exists alone. In addition, the term "at least one" herein means any combination of any one or more of the plurality, or at least two of any combination of the plurality, for example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0030] The present application will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0031] An embodiment of the present application provides a target user identification method, Figure 1 A flowchart of a target user identification method of an embodiment of the present application is shown in the following table. Figure 1 As shown, the method comprises:

[0032] S101: Obtain at least one group of first data of a first user in each interaction stage of at least one interaction stage.

[0033] Here, the first user can be a registered user on the setting application, and the first data is behavior data of the user in an interaction stage. In this embodiment, the interaction stage can be understood as a user role of the first user. The first user has different interaction behaviors in different interaction stages, that is, the first user has different interaction behaviors in different user roles, resulting in different first data in different interaction stages. In actual applications, there can be multiple user roles in a business scenario, and the same first user can have different user roles at the same time.

[0034] In actual applications, the interaction stage corresponds to a business stage in a business cycle. The business cycle is divided according to the business stages of different objects. For example, in a car scenario, the business cycle can be divided into seven different business stages according to the roles of the user in the business stages of the car, which are: buyer, owner, subscriber, audience, visitor, fan, and user. The buyer refers to a user who purchases a car, the owner refers to a user who owns a car, the subscriber refers to a user who uses or subscribes to after-sales services of the car, the audience refers to a user who can see information about the car, the visitor refers to a user who visits a car store, the fan refers to a user who follows a social media account of the car brand, and the user refers to a user of an application or applet of the car, including a registered user and a non-registered user.

[0035] In the car scenario, the first data can be generated by the first user in one of the seven interaction stages. For example, the first user can generate an interaction behavior of visiting a car store in the visitor interaction stage, thereby obtaining a corresponding set of first data.

[0036] In actual applications, in order to more comprehensively obtain behavior data of a user to achieve accurate identification of the user, the behavior data of the user can be obtained from multiple data platforms. In particular, in the car scenario, because relevant information needs to be distributed on different data platforms, behavior data of different users needs to be obtained from multiple data platforms. For example, the second data can be obtained from a car application, the second data can also be obtained from a car applet, and the second data can also be obtained from different social applications.

[0037] The same user registers a related user account on different data platforms, that is, the same user data exists in the second data obtained from different data platforms. Different users are distinguished by user identifiers on different data platforms. Therefore, when obtaining second data from different data platforms, the second data also carries a user identifier, so that the same user data can be identified from the second data according to the user identifier. For example, the second data obtained from the official website carries a data (cookie) identifier stored on the user's local terminal, the second data obtained from the applet carries a user uniform identifier (UnionID), the second data obtained from the public number carries an ordinary user identifier (openid), and the second data obtained from the application carries an advertising identifier (IDFA, IdentifierFor Advertising).

[0038] According to the user identifier carried by the second data, the second data corresponding to the user identifier of the first user is found, and then at least one set of first data of the first user in each interaction stage in at least one interaction stage is obtained.

[0039] In actual application, the user may have one or more user identifiers on different data platforms. The ID mapping technology can process the user identifiers of the user on different data platforms to generate a mapping graph corresponding to different users. In the mapping graph, the same user identifier can be associated. For user identifiers that are not the same in the mapping graph, a valid period can be defined. For example, in the mapping graph of user a, there are two user identifiers A and another user identifier B. Within the valid period, the second data carrying the user identifier B is considered to be the data of user a. When the valid period is exceeded, the second data carrying the user identifier B is considered to be the data of a new user and is processed as such. After constructing the mapping graph of each user, the unique user identifier of each user can be determined according to the priority of different user identifiers. For example, assuming that the priority of openid is the highest, when the same openid exists in the mapping graph of the user, the openid can be used as the unique user identifier of the user.

[0040] The ID mapping technology can associate different user identifiers belonging to the same user. Even if the types of user identifiers carried by different data platforms are different, at least one set of first data belonging to the first user can be determined therefrom.

[0041] In an embodiment, the first field includes at least three fields, wherein the first field is used to indicate the corresponding interaction stage, that is, to record the user role corresponding to the interaction behavior, for example, in the car scenario, the first field can be one of the buyer, the car owner, the subscriber, the audience, the visitor, the fan, and the user, and specifically, assuming that the first data is generated in the case that the first user plays the car information, the first field in the first data can be the audience.

[0042] The second field is used to indicate the interaction level of the interaction behavior, and in an implementation, the interaction level includes a high interaction level, a medium interaction level, and a low interaction level, respectively. The interaction level can be determined according to a preset rule for different interaction actions. For example, the corresponding level can be determined according to the duration of the interaction action. Taking playing as an example, when the duration of playing is long, the corresponding high interaction level is determined, and when the duration of playing is short, the corresponding low interaction level is determined.

[0043] The third field is used to indicate the corresponding interaction channel, wherein the interaction channel is divided into online and offline, and the online interaction channel can be further divided into a private domain, a public domain, and a mixed domain. The private domain refers to obtaining the relevant information of the user, such as a mini program, an application program, or an official website. The public domain refers to not obtaining the relevant information of the user, such as an advertisement. The mixed domain refers to different social media platforms. The offline interaction channel can be further divided into a store and an exhibition. Specifically, assuming that the first data is generated in the case that the first user plays the car information, wherein the car information can be a kind of advertisement, and the corresponding third field is the online public domain.

[0044] In actual application, the first data can further include the following fields:

[0045] The fourth field is used to indicate the corresponding interaction behavior, and in actual application, the fourth field can be used to record the actual action of the interaction behavior, such as playing / opening / arriving / purchasing / test driving / commenting, and the like, which are related to the car scenario. Specifically, assuming that the first data is generated in the case that the first user plays the car information, the fourth field in the first data can be playing, which records the specific action of the interaction behavior.

[0046] The fifth field is used to indicate the interaction content related to the corresponding interaction behavior, which can be understood as the operation object of the interaction behavior, such as a picture / video / store / application / car / tweet, and specifically, assuming that the first data is generated in the case that the first user plays the car information, wherein the car information is presented in the form of a video, and the fifth field of the corresponding first data can be the video. Referring to Figure 2 , Figure 2 The corresponding interaction content of the user in different interaction stages in the car scenario is shown, wherein in Figure 2In the actual application, the first data can be arranged in the form of a data table. Referring to Table 1, Table 1 shows a table for recording the first data in the automobile scenario.

[0047] In the actual application, the first data can be arranged in the form of a data table. Referring to Table 1, Table 1 shows a table for recording the first data in the automobile scenario.

[0048] Table 1

[0049]

[0050] S102: Process the obtained first data based on at least one set dimension to obtain a first score of the first user.

[0051] Here, processing the obtained first data based on at least one set dimension means scoring the first user according to the first data in at least one set dimension, and finally obtaining a first score of the first user. The first score can be used to determine whether the first user is a target user of the set object. The target user refers to a target user who has the intention to purchase the set object. When the first score is greater than a set threshold, the first user can be considered as the target user.

[0052] In this embodiment, the obtained first data can be processed based on different set dimensions to obtain the score of the first user in different dimensions. The score of the first user is obtained by scoring the first user according to the active degree of interaction (first dimension) reflected by the first data, scoring the first user according to the influence degree of interaction (second dimension) reflected by the first data, and scoring the first user according to the ability to dominate the set object (third dimension) reflected by the first data. Thus, the behavior data of the user can be processed through three dimensions, and finally the score of the user can be generated.

[0053] In the actual application, in order to compare different first users, the first score of the first user can be normalized so that the first score of the first user is within the range of 0-100. Thus, different first users can be compared based on the first score under the same standard.

[0054] In an embodiment, as shown in Figure 3 The processing of the obtained first data based on at least one set dimension to obtain the first score of the first user includes:

[0055] S301: Process the obtained first data based on each of the at least one set dimension respectively to obtain a first score corresponding to each dimension.

[0056] In practical applications, the scoring of the first user from the three dimensions is essentially processing the behavior data of the user from the three dimensions to obtain a first score of the first user. For different behavior data, different set dimensions need to be processed. For example, for behavior data of login behavior, browsing behavior, and access behavior, the first dimension needs to be processed to obtain a first score of the first dimension. For behavior data of community content propagation behavior, the second dimension needs to be processed, for example, behavior data related to community content being followed, liked, commented, etc., to obtain a first score of the second dimension. For behavior data of transaction behavior, the third dimension needs to be processed, for example, behavior data of order payment, to obtain a first score of the third dimension.

[0057] In an embodiment, as shown in Figure 4 the processing of the obtained first data based on the dimensions to obtain a first score corresponding to the one set dimension includes:

[0058] S401: Divide the obtained first data into data sets corresponding to each set dimension respectively based on the at least one set dimension.

[0059] Here, for different first data, the user needs to be evaluated from the corresponding set dimension. For example, first data generated by user login, browsing, and access behavior needs to be evaluated from the first dimension, first data generated by user follow, like, comment, and share propagation behavior needs to be evaluated from the second dimension, and first data generated by user consumption behavior needs to be evaluated from the third dimension. In this embodiment, the obtained first data is divided into data sets corresponding to each set dimension respectively, for example, data set A corresponding to the first dimension, which represents that data set A needs to be processed from the first dimension to generate an evaluation of the user.

[0060] In practical applications, the data sets corresponding to each set dimension can be divided according to the user behavior recorded in the first data.

[0061] S402: Process each data set respectively to obtain a second score of the set dimension corresponding to each data set.

[0062] Here, the second score can be regarded as the score of each group of first data in the corresponding set dimension, wherein,

[0063] The target data set is any one of a plurality of data sets, and the target setting dimension is a setting dimension corresponding to the target data set. The second score of the setting dimension corresponding to the data set is described in detail below, taking the target data set as an example. Figure 5 As shown in the following table, the table includes:

[0064] S501: Determine the field weight of each field in each first data in the target data set under the target setting dimension.

[0065] In practical applications, the second score of the target data set can be determined by the interaction stage score of the target data set, the interaction behavior score of the target data set, and the interaction content score of the target data set.

[0066] The interaction stage score of the data set is determined by the first field of the first data and the field weight of the first field. The interaction behavior score of the data set is mainly reflected in the interaction level of the interaction behavior and is determined by the second field of the first field and the field weight of the second field. The interaction content score of the data set is mainly reflected in the interaction channel of the interaction behavior and is determined by the third field and the field weight of the third field.

[0067] In practical applications, when scoring the user in the initial stage, default field weights can be assigned to different fields.

[0068] In an embodiment, the field weight can be obtained from local storage or a server. The field weight corresponding to the setting dimension can be stored locally, and the field weight of each field of the first data can be obtained from the local storage when determining the first score of the first user. Alternatively, the field weight corresponding to the setting dimension can be stored in the server, which can save the local storage space. When determining the first score of the first user, the field weight of each field of the first data can be obtained from the server by exchanging data with the server.

[0069] S502: Based on the determined field weight, weighted sum is performed on each group of first data in the target data set to obtain the second score of the target setting dimension corresponding to the target data set.

[0070] In practical applications, the scores corresponding to different first fields are not the same. For example, under the first dimension, when the field value of the first field is the audience, the score of the first field can be determined as 1. When the field weight of the first field is 100%, the interaction stage score of the data set can be determined as 1 by the score of the first field and the field weight of the first field.

[0071] In the first dimension, when the field value of the second field is of a high interaction level, the score of the second field can be determined as 3, and when the field weight of the first field is 100%, the interaction behavior score of the data set can be determined as 3 by the score of the second field and the field weight of the second field.

[0072] In the first dimension, when the field value of the third field is of a public domain, the score of the third field can be determined as 1, and when the field weight of the first field is 100%, the interaction content score of the data set can be determined as 1 by the score of the third field and the weight of the third field.

[0073] In summary, f(second score) = interaction stage score of the first data * interaction behavior score of the first data * interaction content score of the first data = SUM(weight of the first field * score of the first field) * SUM(weight of the second field * score of the second field) * SUM(weight of the third field * score of the third field).

[0074] In actual application, the score corresponding to each field can also be obtained from a storage table in local storage or a server, as shown in Table 2, which shows a storage table.

[0075] Table 2

[0076] Dimension Element Field Field Value Score Weight First Dimension User First Field Audience 1 100％ First Dimension User First Field Visitor 1 100％ First Dimension User First Field Fan 2 100％ First Dimension User First Field User 2 100％ First Dimension User First Field Buyer 3 100％ First Dimension User First Field Driver 3 100％ First Dimension User First Field Subscriber 3 100％ First Dimension Action Second Field Low 1 100％ First Dimension Action Second Field Medium 2 100％ First Dimension Action Second Field High 3 100％ First Dimension Object Third Field Private Domain 3 100％ First Dimension Object Third Field Public Domain 1 100％ First Dimension Object Third Field Mixed Domain 2 100％

[0077] In an embodiment, in order to obtain a more accurate first score of the first user, the field weight can also be adjusted, and in one way, a first score of a set dimension can be used to adjust the weight value of each field in the first data corresponding to the set dimension. For example, when the first score of the first dimension is greater than a set threshold, it indicates that the first user has a high degree of activity in interaction behavior, that is, the first user will frequently log in to the related application, browse the related content in the application, frequently visit the store, etc. In this case, it is considered that the first user can generate high-quality interaction behavior, and the first user is more inclined to the target user, so the field weight corresponding to each field in the first dimension can be increased.

[0078] S403: Determine a first score corresponding to a set dimension based on the second score of each data set corresponding to the set dimension.

[0079] Here, the processing of each data set is to obtain the second score of each data set corresponding to the set dimension, and the first score of the user corresponding to each set dimension is obtained from the second score of each data set corresponding to the set dimension. For example, based on the second score of the data set in the first dimension, the first score of the user corresponding to the first dimension can be determined, and based on this, f (the first score of the set dimension) = SUM (f (the second score of the set dimension)) can be obtained, that is, the first score of the set dimension is obtained by summing the second score of each data set in the set dimension. For example, the second score of the first dimension corresponding to data set A is a, and the second score of the first dimension corresponding to data set B is b, and the first score of the first user in the first dimension can be determined = a + b.

[0080] In an embodiment, as shown in Figure 6 the method further comprises:

[0081] S601: Push the first task to the first user, and receive the first feedback of the first user executing the first task.

[0082] The second score is obtained by weighting and summing each group of first data according to the field weight of each group of first data. In the initial stage, the same weight value is assigned to the field weight of each set dimension, and in the subsequent stage, the field weight of each set dimension needs to be adjusted to obtain accurate second scores.

[0083] In this embodiment, the first user will be pushed a related first task, wherein the first task is used to encourage the first user to maintain the interaction behavior, for example, the first task requires the first user to log in to the application program and complete the check-in for a week in succession, and in the case that the first user completes the first task, the corresponding user incentive is returned to the first user. At the end of the task time of the first task, the first feedback about the execution of the first task by the first user is received, and the execution of the first task can be determined through the first feedback.

[0084] In actual application, the first task can be pushed to the first user whose first score is higher than a set threshold.

[0085] S602: Adjust the field weight corresponding to each set dimension based on the first feedback.

[0086] Here, the first feedback can be positive feedback, which indicates that the first user completes the first task, and the first feedback can also be negative feedback, which indicates that the first user does not complete the first task, and the field weight corresponding to each set dimension is adjusted according to the first feedback.

[0087] The adjustment of the field weight is described in detail as follows:

[0088] If the first feedback is positive, the field weight of each field in the first data corresponding to each set dimension can be increased. For example, suppose the first task is for the first user to log in to the application and complete the check-in for a week. The check-in is to confirm that the first user has logged in. If the first feedback is positive, it means that the first user has logged in for a week. The user's login behavior needs to be evaluated from the first dimension. Therefore, the field weight of the login behavior corresponding to the first dimension can be increased. The interaction stage corresponding to the first user's login behavior is "user", the interaction level of the login behavior is low interaction level, and the interaction channel corresponding to the login behavior is private domain. That is, the field weights corresponding to "user", "low interaction level", and "private domain" in the first dimension can be increased, while the field weights of other fields remain unchanged.

[0089] If the first feedback is negative, the corresponding field weight can be reduced. For example, suppose the first task is for the first user to log in to the application and complete the check-in for a week. If the first feedback is negative, it means that the first user's login behavior did not last for a week, which is equivalent to the first user browsing the application less frequently. Here, the login behavior corresponds to the interaction stage "user", the interaction level of the login behavior is low, and the interaction channel corresponding to the login behavior is private domain. That is, the field weights corresponding to "user", "low interaction level" and "private domain" in the first dimension can be reduced, while the field weights corresponding to other fields remain unchanged.

[0090] In addition to adjusting the field weights, the second score can be adjusted according to the time decay function. Specifically, the second score = AP + (1-P)B, where A is the second score calculated based on the first data of the previous day, B is the second score calculated based on the first data of the current day, and P is the time decay coefficient. In practical applications, the time decay coefficient can be set according to the task duration of the first task. For example, if the task duration of the first task is one week, then the corresponding time decay coefficient can be set according to 7 days, where P... (t) =P (t) e -at Where t represents the number of days and a represents the corresponding coefficient, taking t=30 as an example, for instance... Figure 6 As shown, Figure 6 The graph shows the decay coefficient, which approaches 0 on day 30.

[0091] S302: The first score corresponding to each of the at least one set dimension is weighted and summed to obtain the first user's first score.

[0092] After determining the first score of the first dimension, the first score of the second dimension, and the first score of the third dimension, a first score of the first user can be determined, specifically, f (the first score) = SUM (S t Weight S), where S t represents the user score of different set dimensions, for example, S1 represents the user score of the first dimension, S2 represents the user score of the second dimension, S3 represents the user score of the third dimension, and Weight S represents the weight of each dimension. In actual application, the initial weight values of the first dimension, the second dimension, and the third dimension are 33%, and f can support four arithmetic operations and power function calculation.

[0093] In an embodiment, the method further comprises:

[0094] In a case where the first score is greater than a first set threshold, a resource delivery task is set for the first user.

[0095] In a case where the first score of the first user is greater than the first set threshold, it can be determined that the first user is a target user of the set object. In this embodiment, the target user is a user who has a will to dominate the set object. For example, in the automobile scenario, the target user refers to a user who has a will to dominate the automobile, that is, a car user who has a will to buy a car or a will to visit a store. In a case where the first user is determined to be the target user, a resource delivery task can be set for the first user, where the resource delivery task can be information with incentive, which is delivered to the first user through online or offline channels, and can make the first user have a stronger will to dominate the set object.

[0096] In the above embodiment, at least one set of first data of the first user in each interaction stage of at least one interaction stage is obtained, the obtained first data is processed based on at least one set dimension, and the first score of the first user is obtained. The behavior data of the user in the business cycle can be analyzed from at least one set dimension, the user can be scored from at least one set dimension, and thus the target user can be accurately identified according to the score of the user.

[0097] To achieve the target user identification method of the embodiments of the present application, the embodiments of the present application further provide a target user identification device, as shown in Figure 7 The device comprises:

[0098] The acquisition unit 701 is configured to acquire at least one set of first data of the first user in each interaction stage of at least one interaction stage. The first data represents behavior data of the first user in the corresponding interaction stage. Each interaction stage of the at least one interaction stage corresponds to one business stage in the business cycle of the set object.

[0099] The determining unit 702 is configured to process the obtained first data based on at least one setting dimension to obtain a first score of the first user; and

[0100] The first score is used to determine whether the first user is a target user of the setting object; and the at least one setting dimension includes a dimension representing an active degree of interaction, a dimension representing an influence degree of interaction, and / or a dimension representing a capability of dominating the setting object.

[0101] In an embodiment, the first data is composed of the following fields:

[0102] A first field, which is used to indicate a corresponding interaction stage;

[0103] A second field, which is used to indicate an interaction level of a corresponding interaction behavior;

[0104] A third field, which is used to indicate a corresponding interaction channel; and the interaction channel is used to transmit interaction information related to the corresponding interaction behavior.

[0105] In an embodiment, when the determining unit 702 processes the obtained first data based on at least one setting dimension to obtain a first score of the first user, the determining unit 702 is configured to:

[0106] Process the obtained first data based on each setting dimension in the at least one setting dimension to obtain a first score corresponding to each dimension;

[0107] Sum up the first scores corresponding to each setting dimension in the at least one setting dimension to obtain the first score of the first user.

[0108] In an embodiment, when the determining unit 702 processes the obtained first data based on one setting dimension to obtain a first score corresponding to the one setting dimension, the determining unit 702 is configured to:

[0109] Divide the obtained first data into a plurality of data sets corresponding to each setting dimension based on the at least one setting dimension;

[0110] Process each data set to obtain a second score of the corresponding setting dimension of each data set, respectively;

[0111] Determine the first score corresponding to the one setting dimension based on the second scores of the corresponding setting dimension of each data set.

[0112] In an embodiment, the target data set is any one of a plurality of data sets, the target setting dimension is a setting dimension corresponding to the target data set, and the determining unit 702, when processing the target data set to obtain a second score of the target setting dimension corresponding to the target data set, is configured to:

[0113] determine a field weight corresponding to each field in each set of first data in the target data set under the target setting dimension;

[0114] perform weighted summation on each set of first data in the target data set based on the determined field weight, to obtain the second score of the target setting dimension corresponding to the target data set.

[0115] In an embodiment, the apparatus further includes:

[0116] a setting unit configured to set a resource delivery task for the first user if the first score is greater than a first setting threshold.

[0117] In an embodiment, the apparatus further includes:

[0118] a pushing unit configured to push a first task to the first user.

[0119] a receiving unit configured to receive a first feedback of the first user performing the first task.

[0120] The adjusting unit is further configured to:

[0121] adjust the field weight corresponding to each setting dimension based on the first feedback.

[0122] In actual application, the obtaining unit 701 and the determining unit 702 can be implemented by a processor in a target user identification apparatus. Of course, the processor needs to run a program stored in a memory to implement the functions of the above program modules.

[0123] It should be noted that the above Figure 7 The target user identification apparatus provided by the embodiments only takes the above division of program modules as an example for illustration, and in actual application, the above processing can be completed by different program modules according to needs, that is, the internal structure of the apparatus is divided into different program modules to complete all or part of the above processing. In addition, the target user identification apparatus and the target user identification method provided by the above embodiments belong to the same concept, and the specific implementation process is described in the method embodiments, which will not be repeated here.

[0124] Based on the hardware implementation of the above program modules, and in order to implement the method of the embodiments of the present application, the embodiments of the present application further provide an electronic device, Figure 8A schematic diagram of a hardware configuration of an electronic device according to an embodiment of the present application is shown in Figure 8 The electronic device comprises:

[0125] A communication interface 1, which is capable of information interaction with other devices such as network devices and the like;

[0126] A processor 2, which is connected with the communication interface 1 to realize information interaction with other devices, and is used to run a computer program to execute the target user identification method provided in one or more technical solutions described above. The computer program is stored on a memory 3.

[0127] Of course, in actual application, various components in the electronic device are coupled together through a bus system 4. It can be understood that the bus system 4 is used to realize the connection and communication between the components. The bus system 4 includes not only a data bus, but also a power supply bus, a control bus and a status signal bus. However, in order to clearly illustrate, all kinds of buses are marked as the bus system 4. Figure 8

[0128] The memory 3 in the embodiment of the present application is used to store various types of data to support the operation of the electronic device. Examples of these data include any computer program used to operate on the electronic device.

[0129] ​It can be understood that the memory 3 can be a volatile memory or a non-volatile memory, and can also include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a ferromagnetic random access memory (FRAM), a flash memory, a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM). The magnetic surface memory can be a disk memory or a tape memory. The volatile memory can be a random access memory (RAM) used as an external cache. By way of example but not limitation, many forms of RAM can be used, such as static random access memory (SRAM), synchronous static random access memory (SSRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), sync link dynamic random access memory (SLDRAM), and direct rambus random access memory (DRRAM).The memory 3 described in the embodiments of the present application is intended to include, but not limited to, these and any other suitable type of memory.

[0130] The method disclosed in the embodiments of the present application can be applied in the processor 2 or implemented by the processor 2. The processor 2 can be an integrated circuit chip with processing capability. In the implementation process, each step of the above method can be completed by the integrated logic circuit of hardware in the processor 2 or the instruction in the form of software. The processor 2 described above can be a general processor, a DSP, or other programmable logic device, discrete gate or transistor logic device, discrete hardware component, etc. The processor 2 can implement or execute the disclosed methods, steps and logic block diagrams in the embodiments of the present application. The general processor can be a microprocessor or any conventional processor, etc. In combination with the steps of the method disclosed in the embodiments of the present application, the execution can be directly completed by the hardware decoding processor or by the combination of hardware and software modules in the decoding processor. The software module can be located in the computer readable storage medium, which is located in the memory 3. The processor 2 reads the program in the memory 3 and combines the hardware to complete the steps of the above method.

[0131] The processor 2 implements the corresponding flow in each method of the embodiments of the present application when executing the program. For brevity, it will not be repeated here.

[0132] In the exemplary embodiments, the embodiments of the present application also provide a computer readable storage medium, for example, including the memory 3 storing the computer program, and the above computer program can be executed by the processor 2 to complete the steps of the above method. The computer readable storage medium can be FRAM, ROM, PROM, EPROM, EEPROM, Flash Memory, magnetic surface memory, optical disc, or CD-ROM, etc.

[0133] In several embodiments provided by the present application, it should be understood that the disclosed apparatus, terminal and method can be implemented by other manners. The apparatus embodiments described above are only schematic, for example, the division of the units is only a logical function division, and there can be another division manner in actual implementation, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the various components shown or discussed can be through some interface, and the indirect coupling or communication connection between the various components can be electrical, mechanical or other forms.

[0134] The units described as separate components above can or can not be physically separate, and the components shown as units can or can not be physical units, i.e., can be located in one place or distributed on multiple network units; part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0135] In addition, each functional unit in each embodiment of the present application can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be realized in the form of hardware or in the form of hardware plus software functional unit.

[0136] Those skilled in the art can understand that all or part of the steps of the above method embodiments can be completed by program instruction related hardware, and the above program can be stored in a computer readable storage medium, and the program executes the steps including the above method embodiments when executed; and the above storage medium includes mobile storage device, ROM, RAM, magnetic disc or optical disc and various storage program codes.

[0137] Alternatively, the integrated unit of the present application, if implemented in the form of a software function module and sold or used as an independent product, can also be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the embodiments of the present application can be embodied in the form of a software product, which is stored in a computer readable storage medium, includes a number of instructions to make an electronic device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the methods described in the embodiments of the present application. The above computer readable storage medium includes mobile storage device, ROM, RAM, magnetic disc or optical disc and various storage program codes.

[0138] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can easily think of changes or replacements within the technical range disclosed in the present application, which should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for identifying target users, characterized in that, include: Obtain at least one set of first data for each interaction phase of at least one interaction phase of the first user; The first data represents the behavioral data of the first user's interactive behavior during the corresponding interaction phase; Each of the at least one interaction phase corresponds to a business phase in the business cycle of the defined object; the user is a user in the automotive scenario. The first data, obtained through processing based on multiple defined dimensions, yields the first user's first rating; whereby... The first rating is used to determine whether the first user is the target user of the defined object; the multiple defined dimensions include a dimension representing the activity level of the interaction, a dimension representing the influence level of the interaction, and a dimension representing the ability to dominate the defined object; The step of processing the first data obtained based on multiple set dimensions to obtain the first user's first score includes: processing the first data obtained based on each of the multiple set dimensions to obtain the first score corresponding to each dimension; The process of processing the obtained first data based on each of the multiple defined dimensions to obtain the first score corresponding to each dimension includes: Based on the multiple defined dimensions, the acquired first data is divided into data sets corresponding to each defined dimension. Each dataset is processed separately to obtain the second score for a given dimension for each dataset. Based on the second score of the set dimension corresponding to each data set, the first score corresponding to the multiple set dimensions is determined.

2. The method according to claim 1, characterized in that, The first data includes at least the following fields: The first field; the first field is used to indicate the corresponding interaction stage; The second field; the second field is used to indicate the interaction level of the corresponding interactive behavior; Third field; The third field is used to indicate the corresponding interaction channel; the interaction channel is used to transmit interaction information related to the corresponding interaction behavior.

3. The method according to claim 1 or 2, characterized in that, The process of obtaining the first user's first rating by processing the first data based on at least one defined dimension further includes: The first score of the first user is obtained by weighted summation of the first scores corresponding to each of the at least one set dimension.

4. The method according to claim 3, characterized in that, The target dataset is any one of multiple datasets, and the target defined dimension is a defined dimension corresponding to the target dataset. The process of processing the target dataset to obtain the second score of the target defined dimension corresponding to the target dataset includes: Determine the field weight of each field in each group of first data in the target dataset under the target defined dimension; Based on the determined field weights, each group of first data in the target dataset is weighted and summed to obtain the second score of the target set dimension corresponding to the target data set.

5. The method according to claim 1, characterized in that, The method further includes: If the first score is greater than the first set threshold, a resource delivery task is set for the first user.

6. The method according to claim 4, characterized in that, The method further includes: Push the first task to the first user and receive the first feedback from the first user on the execution of the first task; Based on the first feedback, adjust the field weights corresponding to each set dimension.

7. A target user identification device, characterized in that, include: The acquisition unit is used to acquire at least one set of first data for each interaction stage of the first user in at least one interaction stage; The first data represents the behavioral data of the first user's interactive behavior during the corresponding interaction phase; Each of the at least one interaction phase corresponds to a business phase in the business cycle of the defined object; the user is a user in the automotive scenario. A determining unit is configured to process the acquired first data based on at least one defined dimension to obtain a first rating for the first user; wherein... The first rating is used to determine whether the first user is the target user of the defined object; the at least one defined dimension includes a dimension representing the activity level of the interaction, a dimension representing the influence level of the interaction, and a dimension representing the ability to dominate the defined object; The step of processing the first data obtained based on multiple set dimensions to obtain the first user's first score includes: processing the first data obtained based on each of the multiple set dimensions to obtain the first score corresponding to each dimension; The process of processing the obtained first data based on each of the multiple defined dimensions to obtain the first score corresponding to each dimension includes: Based on the multiple defined dimensions, the acquired first data is divided into data sets corresponding to each defined dimension. Each dataset is processed separately to obtain the second score for a given dimension for each dataset. Based on the second score of the set dimension corresponding to each data set, the first score corresponding to the multiple set dimensions is determined.

8. An electronic device, characterized in that, include: The processor and the memory used to store computer programs that can run on the processor. When the processor is used to run the computer program, it performs the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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