Credit limit adjustment method, device and electronic device based on user Wifi information

Abstract

The present invention provides a method, device and electronic device for adjusting a credit limit based on user Wifi information, wherein the method for adjusting a credit limit based on user Wifi information includes: obtaining the current credit limit of the user and the Wifi information to which the client associated with the user is connected; determining the credit limit adjustment value of the user according to the Wifi information; and re-determining the credit limit of the user according to the credit limit adjustment value of the user and the current credit limit. Through the technical solution of the present invention, the credit limit can be adjusted for the user in combination with the Wifi information, so that the adjustment of the credit limit is closely related to the actual situation of the user, and the credit limit adjustment result meets the actual needs of the user, and the user does not need to repeatedly and tediously apply for the credit limit, while reducing the credit risk of the lender.

Description

Technical Field

[0001] The present invention relates to the field of Internet financial credit technology, and in particular to a method, device, electronic device and computer-readable storage medium for adjusting a credit limit based on user Wifi information. Background Art

[0002] With the rapid development of Internet finance and the change of people's consumption concepts, advance consumption in the form of credit is becoming more and more accepted by people.

[0003] Credit users generally handle credit business mainly through banks or online credit platforms. Especially for most online credit platforms, when approving user loan amounts, they are generally pure online credit business, and the loan amount usually granted to users is fixed. However, the repayment ability and default risk of each credit user are different. The fixed loan amount will cause an embarrassing situation: if some users with good repayment ability and low default risk are given a low fixed loan amount, it will not only affect the user experience, but also affect the improvement of consumption quality to a certain extent. If you want to increase the loan amount, you need to reapply for a high loan amount. The whole process is very cumbersome and the loan efficiency is low; if some users with poor repayment ability and high default risk are given a high fixed loan amount, it increases the probability of unreasonable consumption by users, greatly increasing the loan risk of lenders. Therefore, how to adjust the credit amount for users has become a technical problem that needs to be solved urgently. Summary of the invention

[0004] The present invention is based on at least one of the above technical problems and proposes a new credit limit adjustment scheme based on user Wifi information. The credit limit can be adjusted according to the user's Wifi information. The credit limit adjustment result meets the user's actual needs and does not require the user to repeatedly and tediously apply for the credit limit, while reducing the credit risk of the lender.

[0005] In view of this, the present invention proposes a new credit limit adjustment method based on user Wifi information, including: obtaining the user's current credit limit and the Wifi information to which the client associated with the user is connected; determining the user's credit limit adjustment value according to the Wifi information; and re-determining the user's credit limit according to the user's credit limit adjustment value and the current credit limit.

[0006] In this technical solution, Wifi information of a client associated with a user is obtained, wherein Wifi is a technology that allows electronic devices to connect to a wireless local area network (WLAN), and is widely used, especially in some large public places, such as airports, railway stations, large shopping malls, etc., which generally provide Wifi hotspots for users to use. Wifi information includes Wifi name, connection time, connection duration, connection frequency, connection time distribution, etc. Through in-depth mining and analysis of Wifi information, the user's travel trajectory can be clarified, the user's actual situation can be obtained, and the user's credit limit adjustment value can be determined based on the Wifi information. The user's credit limit is re-determined based on the user's credit limit adjustment value and the current credit limit obtained, so that the adjustment of the credit limit is closely related to the actual situation of the Wifi user, and the adjustment of the credit limit can vary from person to person. The credit limit adjustment result meets the actual needs of Wifi users, and there is no need for users to repeatedly and tediously apply for credit limits, while reducing the credit risk of lenders.

[0007] In the above technical solution, preferably, the client associated with the user includes a client associated with the user's identity information and / or a client associated with the identity information of the user's associate.

[0008] In any of the above technical solutions, preferably, the step of determining the credit limit adjustment value of the user based on the Wifi information specifically includes: determining the target user group to which the user belongs based on the Wifi information; obtaining the credit limit adjustment value associated with the target user group, and using it as the credit limit adjustment value of the user.

[0009] In this technical solution, users are divided into groups by analyzing Wifi information, and the user's credit limit adjustment value is determined based on the target user group to which the user belongs, so as to ensure the accuracy of the obtained credit limit adjustment value.

[0010] In any of the above technical solutions, preferably, before the step of obtaining the credit limit adjustment value associated with the target user group, it also includes: pre-storing multiple user groups, and the association relationship between each user group of the multiple user groups and the credit limit adjustment value; the step of obtaining the credit limit adjustment value associated with the target user group specifically includes: according to the association relationship, searching for the credit limit adjustment value corresponding to the target user group.

[0011] In this technical solution, by pre-building an association relationship between each user group in multiple user groups and the credit limit adjustment value, the necessary prerequisite is provided for the subsequent accurate positioning of users and the acquisition of precise credit limit adjustment values.

[0012] In any of the above technical solutions, preferably, the step of determining the target user group to which the user belongs based on the Wifi information specifically includes: determining the travel trajectory information of the user based on the Wifi information; and analyzing the travel trajectory information of the user based on a user group positioning model to determine the target user group to which the user belongs.

[0013] In any of the above technical solutions, preferably, before the step of analyzing the travel trajectory information of the user based on the user group positioning model, it also includes: obtaining the travel trajectory information of a large number of users associated with the target place, and the user statistical overdue rate of the target place; based on the user statistical overdue rate of the target place and the travel trajectory information of the large number of users associated with the target place, constructing the user group positioning model, wherein the target place is determined by the analysis of the Wifi information.

[0014] In any of the above technical solutions, preferably, the step of obtaining the user statistical overdue rate of the target location specifically includes: counting the overdue rates of a large number of users associated with the target location as the user statistical overdue rate of the target location.

[0015] In any of the above technical solutions, preferably, the step of constructing the user group positioning model based on the user credit delinquency rate of the target place and the travel trajectory information of the massive users associated with the target place specifically includes: extracting features from the travel trajectory information of the massive users associated with the target place to construct a feature database; and constructing the user group positioning model based on the statistical delinquency rate of the target place and machine simulation training of the feature database.

[0016] In this technical solution, the construction of the user group positioning model provides the necessary prerequisite for achieving accurate division of user groups.

[0017] According to a second aspect of the present invention, a device for adjusting a credit limit based on user Wifi information is proposed, comprising: a first acquisition unit, used to acquire a user's current credit limit and Wifi information to which a client associated with the user is connected; an adjustment unit, used to determine a credit limit adjustment value for the user based on the Wifi information; and a first determination unit, used to redetermine the user's credit limit based on the user's credit limit adjustment value and the current credit limit.

[0018] In this technical solution, Wifi information of a client associated with a user is obtained, wherein Wifi is a technology that allows electronic devices to connect to a wireless local area network (WLAN), and is widely used, especially in some large public places, such as airports, railway stations, large shopping malls, etc., which generally provide Wifi hotspots for users to use. Wifi information includes Wifi name, connection time, connection duration, connection frequency, connection time distribution, etc. Through in-depth mining and analysis of Wifi information, the user's travel trajectory can be clarified, the user's actual situation can be obtained, and the user's credit limit adjustment value can be determined based on the Wifi information. The user's credit limit is re-determined based on the user's credit limit adjustment value and the current credit limit obtained, so that the adjustment of the credit limit is closely related to the actual situation of the Wifi user, and the adjustment of the credit limit can vary from person to person. The credit limit adjustment result meets the actual needs of Wifi users, and there is no need for users to repeatedly and tediously apply for credit limits, while reducing the credit risk of lenders.

[0019] In any of the above technical solutions, preferably, the client associated with the user includes a client associated with the user's identity information and / or a client associated with the identity information of the user's associate.

[0020] In any of the above technical solutions, preferably, the adjustment unit includes: a second determination unit, used to determine the target user group to which the user belongs based on the Wifi information; and a second acquisition unit, used to acquire the credit adjustment value associated with the target user group and use it as the credit adjustment value of the user.

[0021] In this technical solution, users are divided into groups by analyzing Wifi information, and the user's credit limit adjustment value is determined based on the target user group to which the user belongs, so as to ensure the accuracy of the obtained credit limit adjustment value.

[0022] In any of the above technical solutions, preferably, it also includes: a storage unit, used to pre-store multiple user groups, and the association relationship between each user group of the multiple user groups and the credit limit adjustment value; the second acquisition unit, specifically used to search for the credit limit adjustment value corresponding to the target user group according to the association relationship.

[0023] In this technical solution, by pre-building an association relationship between each user group in multiple user groups and the credit limit adjustment value, the necessary prerequisite is provided for the subsequent accurate positioning of users and the acquisition of precise credit limit adjustment values.

[0024] In any of the above technical solutions, preferably, the second determination unit is specifically used to: determine the travel trajectory information of the user according to the Wifi information; and analyze the travel trajectory information of the user based on a user group positioning model to determine the target user group to which the user belongs.

[0025] In any of the above technical solutions, preferably, it also includes: a third acquisition unit, used to obtain the travel trajectory information of a large number of users associated with the target place, and the user statistical overdue rate of the target place; a construction unit, used to construct the user group positioning model based on the user statistical overdue rate of the target place and the travel trajectory information of the large number of users associated with the target place, wherein the target place is determined by parsing the Wifi information.

[0026] In any of the above technical solutions, preferably, the third acquisition unit is specifically used to count the overdue rates of a large number of users associated with the target location as the user statistical overdue rate of the target location.

[0027] In any of the above technical solutions, preferably, the construction unit is specifically used to: extract features from the travel trajectory information of the massive users associated with the target destination to construct a feature database; and construct the user group positioning model based on the statistical overdue rate of the target destination and machine simulation training of the feature database.

[0028] In this technical solution, the construction of the user group positioning model provides the necessary prerequisite for achieving accurate division of user groups.

[0029] According to a third aspect of the present invention, an electronic device is proposed, comprising: a processor; and a memory storing computer executable instructions, wherein the executable instructions, when executed, enable the processor to execute a method as described in any one of the above technical solutions.

[0030] According to a fourth aspect of the present invention, a computer-readable storage medium is proposed, wherein the computer-readable storage medium stores one or more programs, and when the one or more programs are executed by a processor, they implement the method as described in any one of the above technical solutions.

[0031] Through the above technical solution, the credit limit can be adjusted for the user in combination with the Wifi information, so that the adjustment of the credit limit is closely related to the actual situation of the user. The credit limit adjustment result meets the actual needs of the user, and the user does not need to repeatedly and tediously apply for the credit limit, while reducing the credit risk of the lender. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to make the technical problems solved by the present invention, the technical means adopted and the technical effects achieved more clearly, the specific embodiments of the present invention will be described in detail with reference to the accompanying drawings. However, it should be noted that the drawings described below are only drawings of exemplary embodiments of the present invention. For those skilled in the art, drawings of other embodiments can be obtained based on these drawings without creative work.

[0033] Figure 1 A schematic flow chart of a method for adjusting a credit limit based on user Wifi information according to an embodiment of the present invention is shown;

[0034] Figure 2 A schematic block diagram of a device for adjusting a credit limit based on user Wifi information according to an embodiment of the present invention is shown;

[0035] Figure 3 A schematic block diagram of an electronic device according to an embodiment of the present invention is shown;

[0036] Figure 4 A schematic block diagram of a computer-readable storage medium according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0037] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0038] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0039] Exemplary embodiments of the present invention will now be described more fully with reference to the accompanying drawings. However, exemplary embodiments can be implemented in a variety of forms, and should not be construed as limiting the present invention to the embodiments set forth herein. On the contrary, providing these exemplary embodiments enables the present invention to be more comprehensive and complete, and is more convenient for fully conveying the inventive concept to those skilled in the art. The same reference numerals in the figures represent the same or similar elements, components or parts, and thus their repeated description will be omitted.

[0040] Under the premise of being consistent with the technical concept of the present invention, the features, structures, characteristics or other details described in a specific embodiment do not exclude that they can be combined in one or more other embodiments in a suitable manner.

[0041] In the description of specific embodiments, the features, structures, characteristics or other details described in the present invention are intended to enable those skilled in the art to fully understand the embodiments. However, it does not exclude that those skilled in the art can practice the technical solutions of the present invention without one or more of the specific features, structures, characteristics or other details.

[0042] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.

[0043] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0044] It should be understood that although the terms "first", "second", "third", etc. may be used herein to describe various devices, elements, components or parts, this should not be limited by these terms. These terms are used to distinguish one from another. For example, a first device may also be called a second device without departing from the essential technical solution of the present invention.

[0045] The term "and / or" or "and / or" includes any one and all combinations of one or more of the associated listed items.

[0046] Figure 1 A schematic flow chart of a method for adjusting a credit limit based on user Wifi information according to an embodiment of the present invention is shown.

[0047] Wifi is a technology that allows electronic devices to connect to a wireless local area network (WLAN). It is widely used, especially in some large public places, such as airports, railway stations, large shopping malls, etc., which generally provide Wifi hotspots for users to use. Wifi information includes Wifi name, connection time, connection duration, connection frequency, connection time distribution, etc. Through in-depth mining and analysis of Wifi information, the user's travel trajectory can be clarified, the user's actual situation can be obtained, and the user's credit limit can be adjusted based on Wifi information, so that the adjustment of the credit limit is closely related to the actual situation of the Wifi user, and the adjustment result is more in line with the user's actual needs. Specifically, the credit limit adjustment method based on the user's Wifi information is as follows: Figure 1 As shown, the following steps are included:

[0048] Step S102, obtaining the current credit limit of the user and the Wifi information to which the client associated with the user is connected.

[0049] Among them, the client associated with the user includes a client associated with the user's identity information and / or a client associated with the identity information of the user's associates, that is, the Wifi information can come from the client side associated with the user's identity information, or from the client side associated with the identity information of the user's associates, or from a combination of the client side associated with the identity information of the user and the user's associates, so that the user's relevant information can be collected comprehensively and comprehensively during information collection, thereby ensuring the accuracy of the credit limit subsequently re-determined.

[0050] Step S104: Determine the user's credit limit adjustment value based on the Wi-Fi information.

[0051] Before step S104, a user group positioning model is constructed. Specifically, the travel trajectory information of a large number of users associated with the target place and the statistical overdue rate of users in the target place are obtained. Based on the statistical overdue rate of users in the target place and the travel trajectory information of the large number of users associated with the target place, a user group positioning model is constructed. Specifically, by extracting features from the travel trajectory information of the large number of users associated with the target place to construct a feature database, and then constructing the user group positioning model based on the statistical overdue rate of the target place and machine simulation training of the feature database, wherein the target place is determined by parsing Wifi information. Usually, large public places such as airports, railway stations and large shopping malls, etc., the names of the Wifi hotspots supported by them generally directly mark their attributes. For example, an airport is usually a certain airport, a railway station is a certain train, and a shopping mall is a certain shopping. The target place can be determined by the Wifi name, whether it is a certain airport, a certain railway station or a certain shopping mall. The statistical overdue rate of users in the target place can be determined by parsing the overdue rate of the large number of users associated with the target place.

[0052] The implementation process of step S104 specifically includes: determining the target user group to which the user belongs according to the Wifi information, acquiring the credit limit adjustment value associated with the target user group, and using the credit limit adjustment value as the user's credit limit adjustment value.

[0053] Furthermore, before the step of obtaining the credit limit adjustment value associated with the target user group, the method further includes: pre-storing multiple user groups and the association relationship between each user group of the multiple user groups and the credit limit adjustment value. The credit limit adjustment value associated with the target user group is obtained by searching for the credit limit adjustment value corresponding to the target user group according to the association relationship.

[0054] The specific process of determining the target user group to which the user belongs in step S104 includes: determining the user's travel trajectory information based on the Wifi information, and analyzing the user's travel trajectory information based on the user group positioning model to determine the target user group to which the user belongs. The user's accurate positioning is achieved through the constructed user group positioning model.

[0055] Step S106: re-determine the credit limit of the user according to the credit limit adjustment value of the user and the current credit limit.

[0056] For example:

[0057] Embodiment 1: The destination is an airport. By counting the overdue rates of a large number of users who have been to the airport, the user statistical overdue rate rate is obtained. The travel trajectory information of the large number of users associated with the airport is feature extracted to construct a feature database. Specifically, the features in the feature database include the number of visits within a predetermined time period (such as 6 months, 12 months, etc., the predetermined time period can be formulated according to the attributes of the airport, design requirements, etc.) (which can be determined by the airport Wifi connection time, frequency, etc.), airport distribution (specifically in location, such as Beijing, Shanghai or Wuhan, etc., which can be determined by the Wifi name), time distribution (whether it is a holiday, which can be determined specifically according to the Wifi connection time distribution), whether it is a tourist destination (such as Huanglong and Shangri-La airports are tourist airports, and Shanghai Hongqiao is a business airport) ), seat attributes (first class, business class, economy class, etc., which can be obtained by analyzing third-party data), consumption in the airport (which can be obtained by analyzing third-party data), etc., and then build a user group positioning model based on the statistical overdue rate of the target destination and machine simulation training of the feature database. When it is necessary to adjust the credit limit of a new user, the Wi-Fi information of the new user is analyzed to obtain the user's travel trajectory information, and the travel trajectory information of the new user is brought into the user group positioning model to determine the target user group to which the user belongs. If the overdue rate of the target user group to which the user belongs is low, the credit limit can be increased on the basis of the current credit limit. If the overdue rate of the target user group to which the user belongs is high, the credit limit can be reduced on the basis of the current credit limit.

[0058] Embodiment 2: The destination is a certain railway station. By counting the overdue rates of a large number of users who have been to the railway station, the user statistical overdue rate rate is obtained. The travel trajectory information of the large number of users associated with the railway station is feature extracted to construct a feature database. Specifically, the features in the feature database include the number of visits within a predetermined time period (such as 6 months, 12 months, etc., the predetermined time period can be formulated according to the attributes of the railway station, design requirements, etc.) (which can be determined by the Wifi connection time and frequency of the railway station), the distribution of railway stations (specifically in locations, such as Beijing, Shanghai or Wuhan, etc., which can be determined by the Wifi name), the time distribution (whether it is a holiday, which can be determined specifically according to the Wifi connection time distribution), the station attributes (high-speed railway station, ordinary station, intercity station or comprehensive station), the seat attributes (attribute Then, based on the statistical overdue rate of the target destination and the machine simulation training of the feature database, a user group positioning model is constructed. When it is necessary to adjust the credit limit of a new user, the Wi-Fi information of the new user is analyzed to obtain the user's travel trajectory information, and the travel trajectory information of the new user is brought into the user group positioning model to determine the target user group to which the user belongs. If the overdue rate of the target user group to which the user belongs is low, the credit limit can be increased on the basis of the current credit limit. If the overdue rate of the target user group to which the user belongs is high, the credit limit can be reduced on the basis of the current credit limit.

[0059] Embodiment 3: The destination is a shopping mall. By counting the overdue rates of a large number of users who have been to the shopping mall, the user statistical overdue rate rate is obtained. The travel trajectory information of the large number of users associated with the shopping mall is feature extracted to construct a feature database. Specifically, the features in the feature database include the number of visits within a predetermined time period (such as 6 months, 12 months, etc., the predetermined time period can be formulated according to the attributes of the shopping mall, design requirements, etc.) (which can be determined by the shopping mall Wifi connection time, frequency, etc.), the distribution of the shopping mall (specifically in location, such as Beijing, Shanghai or Wuhan, etc., which can be determined by the Wifi name), and the time distribution (whether it is a holiday, which can be determined specifically according to the Wifi connection time distribution). ), consumption in shopping malls (including types of purchased goods, consumption amounts, etc., which can be obtained through analysis of third-party data), etc., and then build a user group positioning model based on the statistical overdue rate of the target location and machine simulation training of the feature database. When it is necessary to adjust the credit limit of a new user, the Wi-Fi information of the new user is analyzed to obtain the user's travel trajectory information, and the travel trajectory information of the new user is brought into the user group positioning model to determine the target user group to which the user belongs. If the overdue rate of the target user group to which the user belongs is low, the credit limit can be increased on the basis of the current credit limit. If the overdue rate of the target user group to which the user belongs is high, the credit limit can be reduced on the basis of the current credit limit.

[0060] Step S106, re-determining the credit limit of the user according to the credit limit adjustment value and the current credit limit of the user.

[0061] Obtain the Wifi information of the client associated with the user, determine the user's credit limit adjustment value based on the Wifi information, and redetermine the user's credit limit based on the user's credit limit adjustment value and the current credit limit obtained. The credit limit adjustment may vary from person to person. The credit limit adjustment result meets the actual needs of Wifi users, and users do not need to repeatedly and tediously apply for credit limits, while reducing the credit risk of lenders.

[0062] Those skilled in the art will appreciate that all or part of the steps to implement the above embodiments are implemented as a program (computer program) executed by a computer data processing device. When the computer program is executed, the above method provided by the present invention can be implemented. Moreover, the computer program can be stored in a computer-readable storage medium, which can be a readable storage medium such as a disk, an optical disk, a ROM, a RAM, or a storage array composed of multiple storage media, such as a disk or a tape storage array. The storage medium is not limited to centralized storage, and it can also be distributed storage, such as cloud storage based on cloud computing.

[0063] The following describes an apparatus embodiment of the present invention, which can be used to execute a method embodiment of the present invention. The details described in the apparatus embodiment of the present invention should be regarded as supplementary to the above method embodiment; details not disclosed in the apparatus embodiment of the present invention can be implemented with reference to the above method embodiment.

[0064] Figure 2 A schematic block diagram of a device for adjusting a credit limit based on user Wifi information according to an embodiment of the present invention is shown.

[0065] like Figure 2 As shown, the credit limit adjustment device 200 based on user Wifi information according to an embodiment of the present invention includes: a first acquisition unit 202, an adjustment unit 204 and a first determination unit 206. The first acquisition unit 202 is used to acquire the current credit limit of the user and the Wifi information to which the client associated with the user is connected; the adjustment unit 204 is used to determine the credit limit adjustment value of the user according to the Wifi information; the first determination unit 206 is used to re-determine the credit limit of the user according to the credit limit adjustment value and the current credit limit of the user. In this embodiment, Wifi is a technology that allows electronic devices to connect to a wireless local area network (WLAN), and is widely used. In particular, some large public places, such as airports, railway stations, and large shopping malls, generally provide Wifi hotspots for users to use. Wifi information includes Wifi name, connection time, connection duration, connection frequency, connection time distribution, etc. Through in-depth mining and analysis of Wifi information, the user's travel trajectory can be clarified, the user's actual situation can be obtained, and the user's credit limit can be adjusted based on the Wifi information, so that the adjustment of the credit limit is closely related to the actual situation of the Wifi user, and the adjustment result is more in line with the actual needs of the user. Therefore, in this embodiment, by obtaining the Wifi information recorded by the client associated with the user, the user's credit limit adjustment value is determined based on the Wifi information, and the user's credit limit is re-determined according to the user's credit limit adjustment value and the obtained current credit limit, so that the adjustment of the credit limit is closely related to the actual situation of the Wifi user, and the adjustment of the credit limit can vary from person to person. The credit limit adjustment result meets the actual needs of the Wifi user, and there is no need for the user to repeatedly and tediously apply for the credit limit, while reducing the credit risk of the lender.

[0066] Among them, the client associated with the user includes a client associated with the user's identity information and / or a client associated with the identity information of the user's associates, that is, the Wifi information can come from the client side associated with the user's identity information, or from the client side associated with the identity information of the user's associates, or from a combination of the client side associated with the identity information of the user and the user's associates, so that the user's relevant information can be collected comprehensively and comprehensively during information collection, thereby ensuring the accuracy of the credit limit subsequently re-determined.

[0067] Furthermore, the adjustment unit 204 includes: a second determination unit 2042, which is used to determine the target user group to which the user belongs according to the Wifi information; and a second acquisition unit 2044, which is used to acquire the credit adjustment value associated with the target user group and use it as the credit adjustment value of the user. By analyzing the Wifi information, the user is divided into groups, and the credit adjustment value of the user is determined based on the target user group to which the user belongs, so as to ensure the accuracy of the acquired credit adjustment value.

[0068] Furthermore, the credit limit adjustment device 200 based on user Wifi information also includes: a storage unit 208, which is used to pre-store multiple user groups, and the association relationship between each user group of the multiple user groups and the credit limit adjustment value; a second acquisition unit 2044 is specifically used to find the credit limit adjustment value corresponding to the target user group according to the association relationship. By pre-building the association relationship between each user group in the multiple user groups and the credit limit adjustment value, the necessary prerequisite is provided for the subsequent accurate positioning of the user and the acquisition of the accurate credit limit adjustment value.

[0069] Furthermore, the second determining unit 2042 is specifically configured to: determine the user's travel trajectory information according to the Wifi information; and analyze the user's travel trajectory information based on the user group positioning model to determine the target user group to which the user belongs.

[0070] Furthermore, the credit limit adjustment device 200 based on user Wifi information also includes: a third acquisition unit 210, used to obtain travel trajectory information of a large number of users associated with the target place, and the statistical overdue rate of users in the target place; a construction unit 212, used to construct a user group positioning model based on the statistical overdue rate of users in the target place and the travel trajectory information of a large number of users associated with the target place, wherein the target place is determined by parsing the Wifi information, usually large public places such as airports, railway stations and large shopping malls, the names of the Wifi hotspots supported by them generally directly mark their attributes, such as an airport is usually a certain airport, a railway station is a certain train, and a shopping mall is a certain shopping. The target place can be determined by the Wifi name.

[0071] Furthermore, the third acquisition unit 210 is specifically configured to count overdue rates of a large number of users associated with the target location as the user statistical overdue rate of the target location.

[0072] Furthermore, the construction unit 212 is specifically used to: extract features from the travel trajectory information of a large number of users associated with the destination to construct a feature database; and construct a user group positioning model based on the statistical overdue rate of the destination and machine simulation training of the feature database. The construction of the user group positioning model provides the necessary prerequisite for achieving accurate division of user groups.

[0073] The following is further described in conjunction with specific embodiments:

[0074] Embodiment 1: The destination is an airport. By counting the overdue rates of a large number of users who have been to the airport, the user statistical overdue rate rate is obtained. The travel trajectory information of the large number of users associated with the airport is feature extracted to construct a feature database. Specifically, the features in the feature database include the number of visits within a predetermined time period (such as 6 months, 12 months, etc., the predetermined time period can be formulated according to the attributes of the airport, design requirements, etc.) (which can be determined by the airport Wifi connection time, frequency, etc.), airport distribution (specifically in location, such as Beijing, Shanghai or Wuhan, etc., which can be determined by the Wifi name), time distribution (whether it is a holiday, which can be determined specifically according to the Wifi connection time distribution), whether it is a tourist destination (such as Huanglong and Shangri-La airports are tourist airports, and Shanghai Hongqiao is a business airport) ), seat attributes (first class, business class, economy class, etc., which can be obtained by analyzing third-party data), consumption in the airport (which can be obtained by analyzing third-party data), etc., and then build a user group positioning model based on the statistical overdue rate of the target destination and machine simulation training of the feature database. When it is necessary to adjust the credit limit of a new user, the Wi-Fi information of the new user is analyzed to obtain the user's travel trajectory information, and the travel trajectory information of the new user is brought into the user group positioning model to determine the target user group to which the user belongs. If the overdue rate of the target user group to which the user belongs is low, the credit limit can be increased on the basis of the current credit limit. If the overdue rate of the target user group to which the user belongs is high, the credit limit can be reduced on the basis of the current credit limit.

[0075] Embodiment 2: The destination is a certain railway station. By counting the overdue rates of a large number of users who have been to the railway station, the user statistical overdue rate rate is obtained. The travel trajectory information of the large number of users associated with the railway station is feature extracted to construct a feature database. Specifically, the features in the feature database include the number of visits within a predetermined time period (such as 6 months, 12 months, etc., the predetermined time period can be formulated according to the attributes of the railway station, design requirements, etc.) (which can be determined by the Wifi connection time and frequency of the railway station), the distribution of railway stations (specifically in locations, such as Beijing, Shanghai or Wuhan, etc., which can be determined by the Wifi name), the time distribution (whether it is a holiday, which can be determined specifically according to the Wifi connection time distribution), the station attributes (high-speed railway station, ordinary station, intercity station or comprehensive station), the seat attributes (attribute Then, based on the statistical overdue rate of the target destination and the machine simulation training of the feature database, a user group positioning model is constructed. When it is necessary to adjust the credit limit of a new user, the Wi-Fi information of the new user is analyzed to obtain the user's travel trajectory information, and the travel trajectory information of the new user is brought into the user group positioning model to determine the target user group to which the user belongs. If the overdue rate of the target user group to which the user belongs is low, the credit limit can be increased on the basis of the current credit limit. If the overdue rate of the target user group to which the user belongs is high, the credit limit can be reduced on the basis of the current credit limit.

[0076] Embodiment 3: The destination is a shopping mall. By counting the overdue rates of a large number of users who have been to the shopping mall, the user statistical overdue rate rate is obtained. The travel trajectory information of the large number of users associated with the shopping mall is feature extracted to construct a feature database. Specifically, the features in the feature database include the number of visits within a predetermined time period (such as 6 months, 12 months, etc., the predetermined time period can be formulated according to the attributes of the shopping mall, design requirements, etc.) (which can be determined by the shopping mall Wifi connection time, frequency, etc.), the distribution of the shopping mall (specifically in location, such as Beijing, Shanghai or Wuhan, etc., which can be determined by the Wifi name), and the time distribution (whether it is a holiday, which can be determined specifically according to the Wifi connection time distribution). ), consumption in shopping malls (including types of purchased goods, consumption amounts, etc., which can be obtained through analysis of third-party data), etc., and then build a user group positioning model based on the statistical overdue rate of the target location and machine simulation training of the feature database. When it is necessary to adjust the credit limit of a new user, the Wi-Fi information of the new user is analyzed to obtain the user's travel trajectory information, and the travel trajectory information of the new user is brought into the user group positioning model to determine the target user group to which the user belongs. If the overdue rate of the target user group to which the user belongs is low, the credit limit can be increased on the basis of the current credit limit. If the overdue rate of the target user group to which the user belongs is high, the credit limit can be reduced on the basis of the current credit limit.

[0077] Those skilled in the art will appreciate that the modules in the above device embodiments may be distributed in the device as described, or may be changed accordingly and distributed in one or more devices different from the above embodiments. The modules in the above embodiments may be combined into one module, or may be further split into multiple submodules.

[0078] Figure 3 1 is a block diagram of an exemplary embodiment of an electronic device according to the present invention. Figure 3 The electronic device 300 according to this embodiment of the present invention is described. Figure 3 The electronic device 300 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0079] like Figure 3 As shown, the electronic device 300 is in the form of a general computing device. The components of the electronic device 300 may include, but are not limited to: at least one processing unit 310, at least one storage unit 320, a bus 330 connecting different system components (including the storage unit 320 and the processing unit 310), a display unit 340, etc.

[0080] The storage unit stores program codes, which can be executed by the processing unit 310, so that the processing unit 310 executes the steps according to various exemplary embodiments of the present invention described in the electronic prescription circulation processing method section of this specification. For example, the processing unit 310 can execute the following steps: Figure 1 Steps shown.

[0081] The storage unit 320 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 3201 and / or a cache memory unit 3202 , and may further include a read-only memory unit (ROM) 3203 .

[0082] The storage unit 320 may also include a program / utility 3204 having a set (at least one) of program modules 3205, such program modules 3205 including but not limited to: an operating system, one or more application programs, other program modules and program data, each of which or some combination may include the implementation of a network environment.

[0083] Bus 330 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0084] The electronic device 300 may also communicate with one or more external devices 400 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 300, and / or communicate with any device that enables the electronic device 300 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 350. Furthermore, the electronic device 300 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 360. The network adapter 360 may communicate with other modules of the electronic device 300 via the bus 330. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 300, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0085] Through the description of the above implementation, it is easy for those skilled in the art to understand that the exemplary embodiments described in the present invention can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present invention can be embodied in the form of a software product, which can be stored in a computer-readable storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, or a network device, etc.) to perform the above method according to the present invention. When the computer program is executed by a data processing device, the computer-readable medium can implement the above method of the present invention, that is: obtain the user's current credit limit and the Wifi information connected to the client associated with the user, determine the user's credit limit adjustment value according to the Wifi information, and re-determine the user's credit limit according to the user's credit limit adjustment value and the current credit limit.

[0086] Figure 4 is a schematic diagram of a computer readable storage medium of the present invention. Figure 4 As shown, the computer program can be stored on one or more computer readable media. The computer readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0087] The computer readable storage medium may include a data signal propagated in a baseband or as part of a carrier wave, wherein a readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The readable storage medium may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by an instruction execution system, an apparatus, or a device or used in combination with it. The program code contained on the readable storage medium may be transmitted with any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the above.

[0088] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0089] In summary, the present invention can be implemented in hardware, or in a software module running on one or more processors, or in a combination thereof. It should be understood by those skilled in the art that general data processing devices such as microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some or all of the components in the embodiments of the present invention. The present invention can also be implemented as a device or apparatus program (e.g., a computer program and a computer program product) for executing part or all of the methods described herein. Such a program implementing the present invention can be stored on a computer-readable medium, or can have the form of one or more signals. Such a signal can be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0090] The specific embodiments described above further describe the purpose, technical solutions and beneficial effects of the present invention in detail. It should be understood that the present invention is not inherently related to any specific computer, virtual device or electronic device, and various general devices can also implement the present invention. The above description is only a specific embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A credit limit adjustment method based on user Wifi information, characterized in that: include: Obtain the user's current credit limit and comprehensively collect Wifi information connected to the client associated with the user; The client associated with the user includes a client associated with the user's identity information and / or a client associated with the user's associated person's identity information, ensuring that the Wifi information comes from the client side associated with the user's identity information, the client side associated with the user's associated person's identity information, and the client side associated with the user's and the user's associated person's identity information; Constructing a user group positioning model, including: parsing Wi-Fi information to determine a target location, obtaining travel trajectory information of a large number of users associated with the target location and a statistical overdue rate of users at the target location, extracting features from the travel trajectory information of a large number of users associated with the target location to construct a feature database, and constructing a user group positioning model based on the statistical overdue rate of the target location and machine simulation training of the feature database; Pre-store multiple user groups and the association relationship between each user group of the multiple user groups and the credit adjustment value; determine the travel trajectory information of the new user according to the Wifi information, and analyze the travel trajectory information of the new user based on the user group positioning model to determine the target user group to which the new user belongs; and, according to the association relationship, search for the credit adjustment value corresponding to the target user group to obtain the credit adjustment value associated with the target user group and use it as the credit adjustment value of the new user; The credit limit of the user is re-determined based on the credit limit adjustment value of the new user and the current credit limit; if the overdue rate of the target user group to which the new user belongs is relatively high, the credit limit is reduced based on the current credit limit; if the overdue rate of the target user group to which the new user belongs is relatively low, the credit limit is increased based on the current credit limit.

2. The method according to claim 1, characterized in that The overdue rates of users in the target area include The overdue rates of a large number of users associated with the target location are counted as the user statistical overdue rate of the target location.

3. A credit limit adjustment device based on user Wifi information, characterized in that: include: A first acquisition unit is used to acquire the current credit limit of the user and comprehensively collect Wifi information connected to the client associated with the user; The client associated with the user includes a client associated with the user's identity information and / or a client associated with the user's associated person's identity information, ensuring that the Wifi information comes from the client side associated with the user's identity information, the client side associated with the user's associated person's identity information, and the client side associated with the user's and the user's associated person's identity information; A third acquisition unit is used to parse the Wi-Fi information to determine the target location, obtain the travel trajectory information of a large number of users associated with the target location, and the user statistical overdue rate of the target location; A construction unit, configured to extract features from the travel trajectory information of a large number of users associated with a destination to construct a feature database, and to construct a user group positioning model based on the overdue rate of the destination statistics and machine simulation training of the feature database; A storage unit, used to pre-store a plurality of user groups and an association relationship between each of the plurality of user groups and a credit limit adjustment value; The adjustment unit includes a second determination unit, configured to determine the travel trajectory information of the new user according to the Wifi information, and analyze the travel trajectory information of the new user based on the user group positioning model to determine the target user group to which the new user belongs; and a second acquisition unit, configured to search for the credit adjustment value corresponding to the target user group according to the association relationship to acquire the credit adjustment value associated with the target user group and use it as the credit adjustment value of the new user; The first determination unit is used to redetermine the credit limit of the user according to the credit limit adjustment value of the user and the current credit limit; wherein, if the overdue rate of the target user group to which the new user belongs is relatively high, the credit limit is reduced based on the current credit limit; if the overdue rate of the target user group to which the new user belongs is relatively low, the credit limit is increased based on the current credit limit.

4. The device according to claim 3, characterized in that The third acquisition unit also includes: a unit for counting overdue rates of a large number of users associated with the target location as a user statistical overdue rate of the target location.

5. An electronic device, characterized in that: include: processor; as well as A memory storing computer executable instructions, which when executed cause the processor to perform the method of any one of claims 1-2.

6. A computer-readable storage medium, wherein: The computer-readable storage medium stores one or more programs, and when the one or more programs are executed by a processor, the method of any one of claims 1-2 is implemented.

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