CN113610631A - User policy adjustment method and device and electronic equipment - Google Patents
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Abstract
The disclosure relates to a user policy adjustment method, a user policy adjustment device, an electronic device and a computer readable medium. The method comprises the following steps: acquiring user information of a user meeting a preset condition, wherein the user information comprises basic information, behavior information and remote information; generating a plurality of local feature parameters of the user based on the behavior information; generating a plurality of remote characteristic parameters of the user based on the remote information; comparing the local characteristic parameters, the remote characteristic parameters and the preset strategies to determine a target strategy; and adjusting the current user policy of the user based on the target policy. The user strategy adjusting method, the user strategy adjusting device, the electronic equipment and the computer readable medium can reduce the customer attrition rate, increase the activity and support enthusiasm of customers, improve the customer satisfaction degree, reduce the labor cost and improve the utilization rate of server resources.
Description
Technical Field
The present disclosure relates to the field of computer information processing, and in particular, to a user policy adjustment method, apparatus, electronic device, and computer readable medium.
Background
With the rapid development of technologies such as internet, internet of things, sensors, etc., a great deal of data is generated in production and life, and people hope to mine valuable information from the data. However, many of the data are characterized by large number of samples and high feature dimension, which undoubtedly increases the difficulty of data mining. In order to solve the above problems, researchers often delete irrelevant and redundant feature information in data by a feature selection method, so that feature dimensions, noise interference and algorithm complexity are reduced, and a model is simple and easy to understand. Feature selection has become a research hotspot in the fields of data mining, artificial intelligence, fault diagnosis and the like. The traditional feature selection algorithm has the defects that the accuracy of the selected feature subset is low when the classification task is carried out, or the size of the selected feature subset is large. For the financial services institution, the resource allocation activity of the user is likely to bring resource risks to the financial services company. The financial service institution urgently needs to analyze the security risk of the user so as to guide the user policy distribution of the user, wherein the user policy can comprise parameters such as resource quota, resource distribution time limit, resource return increase ratio and the like. By risking the risk of the user, it may be decided whether to grant the new applicant credit and how to adjust the credit limit.
Experience shows that due to the fact that difference between the three-party data and the internal behavior data performance and the credit time difference is not large in a short time after the client gives credit, setting a short quota validity period (such as 30 days) for the client can effectively improve the dynamic support passing rate of the user, reduce the complexity of a dynamic support strategy and reduce the overall risk. However, the client with the expired quota needs the user to initiate the credit process again and the loss rate in the middle is very high, so how to effectively manage the client with the expired quota is also a big subject of increasing the balance scale.
The above information disclosed in this background section is only for enhancement of understanding of the background of the disclosure and therefore it may contain information that does not constitute prior art that is already known to a person of ordinary skill in the art.
Disclosure of Invention
In view of this, the present disclosure provides a user policy adjustment method, device, electronic device, and computer readable medium, which can reduce the customer churn rate, increase the activity and expenditure enthusiasm of customers, reduce labor cost, ensure the resource safety of financial service companies, increase the resource utilization efficiency, reduce resource consumption, enable financial companies to have more resources and energy to serve customers, and improve customer satisfaction.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows, or in part will be obvious from the description, or may be learned by practice of the disclosure.
According to an aspect of the present disclosure, a method for adjusting a user policy is provided, where the method includes: acquiring user information of a user meeting a preset condition, wherein the user information comprises basic information, behavior information and remote information; generating a plurality of local feature parameters of the user based on the behavior information; generating a plurality of remote characteristic parameters of the user based on the remote information; comparing the local characteristic parameters, the remote characteristic parameters and the preset strategies to determine a target strategy; and adjusting the current user policy of the user based on the target policy.
Optionally, the method further comprises: acquiring user information of a plurality of historical users; and generating the preset strategies based on the user information of the historical users.
Optionally, generating the plurality of preset policies based on the user information of the plurality of historical users includes: behavior information in the user information of the plurality of historical users is obtained; determining time ranges in the plurality of preset strategies based on time data in the behavior information.
Optionally, generating the plurality of preset policies based on the user information of the plurality of historical users includes: acquiring remote information in the user information of the plurality of historical users; and determining the service characteristics in the preset strategies based on the time data in the remote information.
Optionally, generating the plurality of preset policies based on the user information of the plurality of historical users includes: acquiring basic information in the user information of the plurality of historical users; and generating a blacklist in the plurality of preset strategies based on the basic information.
Optionally, comparing the plurality of local feature parameters, the plurality of remote feature parameters, and the plurality of preset policies, and determining a target policy, includes: and when the local characteristic parameters are within the time range in the preset strategy, the remote characteristic parameters do not contain the service characteristics in the preset strategy, and the user without the preset behavior allocates a first target strategy.
Optionally, comparing the plurality of local feature parameters, the plurality of remote feature parameters, and the plurality of preset policies, and determining a target policy, includes: and when the local characteristic parameters are within the time range in the preset strategy, the remote characteristic parameters do not contain the service characteristics in the preset strategy, and the user containing the preset behavior allocates a second target strategy.
Optionally, comparing the plurality of local feature parameters, the plurality of remote feature parameters, and the plurality of preset policies, and determining a target policy, includes: extracting historical user information of users who are not in the blacklist when the local characteristic parameters are not in the time range of the preset strategy and the remote characteristic parameters do not contain the service characteristics in the preset strategy; and when the historical user information meets a preset condition, distributing a third target strategy for the user.
Optionally, adjusting the current user policy of the user based on the target policy includes: adjusting the current quota of the user based on the target strategy; adjusting the current period of the user based on the target strategy; and adjusting the current special shared resource information of the user based on the target strategy.
According to an aspect of the present disclosure, a user policy adjusting apparatus is provided, the apparatus including: the information module is used for acquiring user information of a user meeting preset conditions, wherein the user information comprises basic information, behavior information and remote information; a local module for generating a plurality of local feature parameters of the user based on the behavior information; a remote module for generating a plurality of remote characteristic parameters of the user based on the remote information; the strategy module is used for comparing the local characteristic parameters, the remote characteristic parameters and the preset strategies to determine a target strategy; and the adjusting module is used for adjusting the current user strategy of the user based on the target strategy.
According to an aspect of the present disclosure, an electronic device is provided, the electronic device including: one or more processors; storage means for storing one or more programs; when executed by one or more processors, cause the one or more processors to implement a method as above.
According to an aspect of the disclosure, a computer-readable medium is proposed, on which a computer program is stored, which program, when being executed by a processor, carries out the method as above.
According to the user policy adjusting method, device, electronic equipment and computer readable medium disclosed by the disclosure, user information of a user meeting a preset condition is acquired, wherein the user information comprises basic information, behavior information and remote information; generating a plurality of local feature parameters of the user based on the behavior information; generating a plurality of remote characteristic parameters of the user based on the remote information; comparing the local characteristic parameters, the remote characteristic parameters and the preset strategies to determine a target strategy; the current user strategy of the user is adjusted based on the target strategy, so that the loss rate of the user can be reduced, the initiative of the user is increased, the satisfaction degree of the user is improved, the labor cost is reduced, the resource utilization rate of a server is improved, the resource safety of a financial service company is guaranteed, the resource utilization efficiency is improved, the resource loss is reduced, the financial company can have more resources and energy to serve the user, and the satisfaction degree of the user is improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The above and other objects, features and advantages of the present disclosure will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings. The drawings described below are merely some embodiments of the present disclosure, and other drawings may be derived from those drawings by those of ordinary skill in the art without inventive effort.
Fig. 1 is a system block diagram illustrating a user policy adjustment method and apparatus according to an exemplary embodiment.
Fig. 2 is a flow chart illustrating a user policy adjustment method according to an exemplary embodiment.
Fig. 3 is a flowchart illustrating a user policy adjustment method according to another exemplary embodiment.
Fig. 4 is a block diagram illustrating a user policy adjustment apparatus according to an example embodiment.
FIG. 5 is a block diagram illustrating an electronic device in accordance with an example embodiment.
FIG. 6 is a block diagram illustrating a computer-readable medium in accordance with an example embodiment.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The same reference numerals denote the same or similar parts in the drawings, and thus, a repetitive description thereof will be omitted.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the subject matter of the present disclosure can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and so forth. In other instances, well-known methods, devices, implementations, or operations have not been shown or described in detail to avoid obscuring aspects of the disclosure.
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
It will be understood that, although the terms first, second, third, etc. may be used herein to describe various components, these components should not be limited by these terms. These terms are used to distinguish one element from another. Thus, a first component discussed below may be termed a second component without departing from the teachings of the disclosed concept. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
It is to be understood by those skilled in the art that the drawings are merely schematic representations of exemplary embodiments, and that the blocks or processes shown in the drawings are not necessarily required to practice the present disclosure and are, therefore, not intended to limit the scope of the present disclosure.
In this disclosure, resources refer to any substance, information, time that may be utilized, information resources including computing resources and various types of data resources. The data resources include various private data in various domains. The innovation of the present disclosure is how to use information interaction technology between the server and the client to make the process of user policy adjustment more automated, efficient and reduce human cost. Thus, the present disclosure can be applied to all kinds of user policy adjustments, including physical goods, water, electricity, and meaningful data, essentially. However, for convenience, the implementation of the user policy adjustment is described in the present disclosure by taking financial data resources as an example, but those skilled in the art will understand that the present disclosure can also be used for allocation of other resources.
The user policy adjustment method provided by the embodiment of the disclosure (for convenience of description, the method provided by the embodiment of the present application may be abbreviated) may be applicable to any application field of a plurality of application fields such as investment, bank, insurance, securities, and e-commerce. In various application domains, the application scenarios involved may include, but are not limited to, login, registration, pre-loan, mid-loan, post-loan, holiday activities, or promotional activities, among others. The method provided by the embodiment of the application can be applied to the generation of the risk decision rule of any business type of any application scenario.
Here, the user policy is a rule for specifically selecting which risk control policy is based on in the risk decision process. For convenience of description, the following description will be made taking the user policy as an example. Different user strategy generation models can be suitable for different application scenes and the generation of user strategies of various services under various application scenes, and the flexibility is high. The user policy-based generation model can output risk decision rules according to which risk control is performed on a specific service according to real-time user data of the specific service in a specific application scenario, where the service may be various services provided to a user in a plurality of application fields such as investment, bank, insurance, securities, and e-commerce, for example, insurance application and loan application. Correspondingly, taking application as an example, the application scenario corresponding to the service may include, but is not limited to, account registration, account login, application for application, approval for application, generation and maintenance of policy, and the like. The application scenarios are only examples, but not exhaustive, and may be determined according to actual application scenarios. The user data includes, but is not limited to, service account information of the user, page operation data of the user, service access duration of the user, service access frequency of the user, terminal device identification information of the user, and region information where the user is located, and may be specifically determined according to an actual application scenario, and is not limited herein.
Fig. 1 is a system block diagram illustrating a user policy adjustment method and apparatus according to an exemplary embodiment.
As shown in fig. 1, the system architecture 10 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal devices 101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The terminal devices 101, 102, 103 may have various communication client applications installed thereon, such as a financial services application, a shopping application, a web browser application, an instant messaging tool, a mailbox client, social platform software, and the like.
The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 105 may be a server that provides various services, such as a background management server that supports financial services websites browsed by the user using the terminal apparatuses 101, 102, and 103. The backend management server may analyze the received user data, and feed back the processing result (e.g., the user policy) to the administrator of the financial services website and/or the terminal device 101, 102, 103.
The server 105 may, for example, obtain user information of a user meeting a preset condition, where the user information includes basic information, behavior information, and remote information; server 105 may generate a plurality of local feature parameters for the user, e.g., based on the behavior information; server 105 may generate a plurality of remote characteristic parameters for the user, e.g., based on the remote information; the server 105 may, for example, compare the local feature parameters, the remote feature parameters, and the preset policies to determine a target policy; server 105 may adjust the user's current user policy, for example, based on the target policy.
The server 105 may also, for example, obtain user information for a plurality of historical users; and generating the preset strategies based on the user information of the historical users.
The server 105 may be a server of one entity, and may also be composed of a plurality of servers, for example, a part of the server 105 may be, for example, used as a user policy system in the present disclosure, and is configured to compare the plurality of local feature parameters, the plurality of remote feature parameters, and the plurality of preset policies, and determine a target policy; adjusting the current user policy of the user based on the target policy; a portion of the server 105 may also be, for example, a preset policy system in the present disclosure, configured to generate the plurality of preset policies based on the user information of the plurality of historical users.
It should be noted that, the user policy adjustment method provided by the embodiment of the present disclosure may be executed by the server 105, and accordingly, the user policy adjustment device may be disposed in the server 105. And the web page end provided for the user to browse the financial service platform is generally positioned in the terminal equipment 101, 102 and 103.
Fig. 2 is a flow chart illustrating a user policy adjustment method according to an exemplary embodiment. The user policy adjustment method 20 includes at least steps S202 to S208.
As shown in fig. 2, in S202, user information of a user meeting a preset condition is acquired, where the user information includes basic information, behavior information, and remote information.
In the embodiment of the present disclosure, the user may be an individual user or an enterprise user, and the allocation of the resource amount may be adjustment of a financial resource amount, or allocation of an electric power resource and a hydraulic resource. The user information may include basic information, such as service account information, terminal device identification information of the user, region information where the user is located, and the like; the user information may also include behavior information, which may be, for example, page operation data of the user, service access duration of the user, service access frequency of the user, and the like, and specific content of the user information may be determined according to an actual application scenario, which is not limited herein. More specifically, the user information of the current user can be obtained in a webpage point burying mode based on user authorization. The remote information can be user data of the user on other transaction platforms or other business departments.
More specifically, behavior information of a user on a website can be acquired through a Fiddler tool, the Fiddler tool works in a web proxy server mode, a client side firstly sends out request data, the Fiddler proxy server intercepts a data packet, and the proxy server impersonates the client side to send data to a server; similarly, the server returns the response data, and the proxy server intercepts the data and returns the intercepted data to the client. And the Fiddler can acquire the related browsing data of residence time, residence page, click operation and the like of the user network browsing.
In S204, a plurality of local feature parameters of the user are generated based on the behavior information. A plurality of local feature information may be generated based on the behavior information and a behavior feature policy. The behavior information can be subjected to data cleaning and data fusion so as to be converted into local characteristic parameters, and more particularly, the behavior information can be subjected to variable loss rate analysis and processing and abnormal value processing; and performing WOE conversion, discrete variable WOE conversion, text variable processing, text variable word2vec processing and the like on the behavior information of the continuous variable discretization.
Among them, WOE is "Weight of Evidence", i.e., Evidence Weight. WOE is a form of encoding of the original features. To WOE encode a feature, this variable needs to be first grouped. Word2vec, a group of correlation models used to generate Word vectors. These models are shallow, two-layer neural networks that are trained to reconstruct linguistic word text. The word2vec model may be used to map each word to a vector, which may be used to represent word-to-word relationships.
In S206, a plurality of remote feature parameters of the user are generated based on the remote information. Data cleaning and data fusion can be performed on the user information to convert the user information into remote characteristic parameters. A plurality of remote feature parameters may be generated based on the remote information and a behavioral remote policy.
In one embodiment, a criticality index of the behavioral information or remote information may be calculated, for example; and extracting partial information from the historical user information based on the criticality index to generate a plurality of historical local characteristic information and historical remote characteristic information. Generating the local feature policy based on a relationship between the plurality of historical local feature information and the historical user information; generating the remote feature policy based on a relationship between the plurality of historical remote feature information and the historical user information.
More specifically, the variable parameters, the discrimination parameters, the information values and the model characteristic parameters of the historical local characteristic information and the historical remote characteristic information can be calculated; and the representative historical local characteristic information and the historical remote characteristic information in the plurality of historical characteristic information are selected based on the variable parameters, the discrimination parameters, the information values and the model characteristic parameters.
The method can comprehensively consider in multiple aspects such as variable coverage, single value coverage, correlation and significance with the target variable, the distinguishing degree (KS) and Information Value (IV) of the target variable, the characteristic importance of tree models (such as XGboost, RF and the like), and the like, and screen the characteristics with high coverage and obvious distinguishing effect on the target variable as the finally used user characteristics.
In S208, the local feature parameters, the remote feature parameters, and the preset policies are compared to determine a target policy.
In one embodiment, for example, when the local feature parameter is within a time range in a preset policy, and the remote feature parameter does not include a traffic feature in the preset policy, and a user without a preset behavior allocates the first target policy.
In one embodiment, for example, when the local feature parameter is within a time range in a preset policy, and the remote feature parameter does not include a traffic feature in the preset policy, a user including a preset behavior assigns a second target policy.
In one embodiment, for example, when the local feature parameter is not in the time range of the preset policy, and the remote feature parameter does not include the service feature in the preset policy, and the user not in the blacklist extracts the historical user information; and when the historical user information meets a preset condition, distributing a third target strategy for the user.
In S210, the current user policy of the user is adjusted based on the target policy. The method comprises the following steps: adjusting the current quota of the user based on the target strategy; adjusting the current period of the user based on the target strategy; and adjusting the current special shared resource information of the user based on the target strategy.
The validity period of the resource limit of the current common user is 60 days, and the historical data shows that the larger the credit movement time difference is within 90 days after credit, the lower the risk of the client is, so that for the first user, namely the user who does not move on the business in 60 days, when the user is not managed and overdue on other business lines and does not enter a blacklist, the user strategy can be prolonged, and more specifically, the resource return validity period can be prolonged to 90 days;
for the client which has moved and settled in 60 days, has no management and no overdue on other service lines, does not enter a blacklist client, can be used as a second user, the repayment situation of the user is good, the resource requirement is short, the user strategy of the second user can be adjusted, more specifically, the resource limit can be improved, the specific improvement proportion can be obtained by calculating the user risk value, wherein the user risk data can be obtained by calculating the user risk model, and the limit validity period is maintained to be 60 days.
The user risk model can be generated through the repayment label or the behavior characteristic of the historical second user and the training of the machine learning model.
For clients who have moved and are not settled, the payment data of the clients in at least 6 periods of the business line can be observed, the clients who pay on time in each period, have no management and no overdue on other business lines and do not enter a blacklist client can be used as third users, resources in user strategies and resource occupation time can be prolonged, and for example, a resource limit with the validity period of 60 days is added.
According to the user strategy adjusting method, user information of a user meeting a preset condition is obtained, wherein the user information comprises basic information, behavior information and remote information; generating a plurality of local feature parameters of the user based on the behavior information; generating a plurality of remote characteristic parameters of the user based on the remote information; comparing the local characteristic parameters, the remote characteristic parameters and the preset strategies to determine a target strategy; the current user strategy of the user is adjusted based on the target strategy, so that the customer attrition rate can be reduced, the initiative of the customer on move and expenditure can be increased, the customer satisfaction can be improved, the labor cost can be reduced, and the resource utilization rate of the server can be improved.
It should be clearly understood that this disclosure describes how to make and use particular examples, but the principles of this disclosure are not limited to any details of these examples. Rather, these principles can be applied to many other embodiments based on the teachings of the present disclosure.
Fig. 3 is a flowchart illustrating a user policy adjustment method according to another exemplary embodiment. The flow 30 shown in fig. 3 is a detailed description of "generating a plurality of preset policies".
As shown in fig. 3, in S302, user information of a plurality of history users is acquired. The remote information of the user on other platforms or service lines can be asynchronously called through the API interface, and the basic information and the behavior information of the user can be acquired through the local log.
In S304, behavior information, remote information, and basic information in the user information of the plurality of history users are acquired.
In S306, a plurality of preset policies are generated based on the analysis of the behavior information, the remote information, and the basic information.
In one embodiment, behavior information in the user information of the plurality of historical users may be obtained, for example; determining time ranges in the plurality of preset strategies based on time data in the behavior information. For example, behavior data of a historical user is analyzed to obtain statistical data, the occurrence time of the user action and branch behavior after the user strategy is adjusted is determined according to a statistical rule, and then the time range in the preset strategy is determined. The time range may be, for example, the length of the resource occupation time or the effective time of the specific resource information.
In one embodiment, for example, remote information among the user information of the plurality of historical users may be obtained; and determining the service characteristics in the preset strategies based on the time data in the remote information. The remote information of the historical user can be analyzed, for example, resource repayment performance of the user on other service platforms or dynamic performance of the user in other types of products can be analyzed, and then the specific resource information which is sensitive to the specific resource information performance of the user can be determined, and further the specific resource information which can most promote the dynamic performance of the user can be included in the user strategy.
In one embodiment, for example, basic information in the user information of the plurality of historical users may be obtained; and generating a blacklist in the plurality of preset strategies based on the basic information. The users with bad credit records or poor repayment performance can be put into a blacklist, and subsequent processing is not carried out on the users, so that resource loss is avoided.
Those skilled in the art will appreciate that all or part of the steps implementing the above embodiments are implemented as computer programs executed by a CPU. When executed by the CPU, performs the functions defined by the above-described methods provided by the present disclosure. The program may be stored in a computer readable storage medium, which may be a read-only memory, a magnetic or optical disk, or the like.
Furthermore, it should be noted that the above-mentioned figures are only schematic illustrations of the processes involved in the methods according to exemplary embodiments of the present disclosure, and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple modules.
The following are embodiments of the disclosed apparatus that may be used to perform embodiments of the disclosed methods. For details not disclosed in the embodiments of the apparatus of the present disclosure, refer to the embodiments of the method of the present disclosure.
Fig. 4 is a block diagram illustrating a user policy adjustment apparatus according to an example embodiment. As shown in fig. 4, the user policy adjustment device 40 includes: an information module 402, a local module 404, a remote module 406, a policy module 408, and an adjustment module 410.
The information module 402 is configured to obtain user information of a user meeting a preset condition, where the user information includes basic information, behavior information, and remote information;
a local module 404 for generating a plurality of local feature parameters of the user based on the behavior information;
the remote module 406 is configured to generate a plurality of remote feature parameters of the user based on the remote information;
the policy module 408 is configured to compare the plurality of local feature parameters, the plurality of remote feature parameters, and the plurality of preset policies, and determine a target policy;
in one embodiment, for example, when the local feature parameter is within a time range in a preset policy, and the remote feature parameter does not include a traffic feature in the preset policy, and a user without a preset behavior allocates the first target policy.
In one embodiment, for example, when the local feature parameter is within a time range in a preset policy, and the remote feature parameter does not include a traffic feature in the preset policy, a user including a preset behavior assigns a second target policy.
In one embodiment, for example, when the local feature parameter is not in the time range of the preset policy, and the remote feature parameter does not include the service feature in the preset policy, and the user not in the blacklist extracts the historical user information; and when the historical user information meets a preset condition, distributing a third target strategy for the user.
The adjustment module 410 is configured to adjust the current user policy of the user based on the target policy. The adjusting module 410 is further configured to adjust the current quota of the user based on the target policy; adjusting the current period of the user based on the target strategy; and adjusting the current special shared resource information of the user based on the target strategy.
According to the user strategy adjusting device, user information of a user meeting preset conditions is obtained, wherein the user information comprises basic information, behavior information and remote information; generating a plurality of local feature parameters of the user based on the behavior information; generating a plurality of remote characteristic parameters of the user based on the remote information; comparing the local characteristic parameters, the remote characteristic parameters and the preset strategies to determine a target strategy; the current user strategy of the user is adjusted based on the target strategy, so that the customer attrition rate can be reduced, the initiative of the customer on move and expenditure can be increased, the customer satisfaction can be improved, the labor cost can be reduced, and the resource utilization rate of the server can be improved.
FIG. 5 is a block diagram illustrating an electronic device in accordance with an example embodiment.
An electronic device 500 according to this embodiment of the disclosure is described below with reference to fig. 5. The electronic device 500 shown in fig. 5 is only an example and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 5, the electronic device 500 is embodied in the form of a general purpose computing device. The components of the electronic device 500 may include, but are not limited to: at least one processing unit 510, at least one memory unit 520, a bus 530 that couples various system components including the memory unit 520 and the processing unit 510, a display unit 540, and the like.
Wherein the storage unit stores program code that is executable by the processing unit 510 to cause the processing unit 510 to perform steps according to various exemplary embodiments of the present disclosure in the present specification. For example, the processing unit 510 may perform the steps as shown in fig. 2, fig. 3.
The memory unit 520 may include a readable medium in the form of a volatile memory unit, such as a random access memory unit (RAM)5201 and/or a cache memory unit 5202, and may further include a read only memory unit (ROM) 5203.
The memory unit 520 may also include a program/utility 5204 having a set (at least one) of program modules 5205, such program modules 5205 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 thereof, may comprise an implementation of a network environment.
The electronic device 500 may also communicate with one or more external devices 500' (e.g., keyboard, pointing device, bluetooth device, etc.), such that a user can communicate with devices with which the electronic device 500 interacts, and/or any devices (e.g., router, modem, etc.) with which the electronic device 500 can communicate with one or more other computing devices. Such communication may occur via input/output (I/O) interfaces 550. Also, the electronic device 500 may 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 the network adapter 560. The network adapter 560 may communicate with other modules of the electronic device 500 via the bus 530. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 500, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, as shown in fig. 6, the technical solution according to the embodiment of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, or a network device, etc.) to execute the above method according to the embodiment of the present disclosure.
The software product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable storage medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable storage medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind 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).
The computer readable medium carries one or more programs which, when executed by a device, cause the computer readable medium to perform the functions of: acquiring user information of a user meeting a preset condition, wherein the user information comprises basic information, behavior information and remote information; generating a plurality of local feature parameters of the user based on the behavior information; generating a plurality of remote characteristic parameters of the user based on the remote information; comparing the local characteristic parameters, the remote characteristic parameters and the preset strategies to determine a target strategy; and adjusting the current user policy of the user based on the target policy.
Those skilled in the art will appreciate that the modules described above may be distributed in the apparatus according to the description of the embodiments, or may be modified accordingly in one or more apparatuses unique from the embodiments. The modules of the above embodiments may be combined into one module, or further split into multiple sub-modules.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.
Exemplary embodiments of the present disclosure are specifically illustrated and described above. It is to be understood that the present disclosure is not limited to the precise arrangements, instrumentalities, or instrumentalities described herein; on the contrary, the disclosure is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
Claims (12)
1. A user policy adjustment method is characterized by comprising the following steps:
acquiring user information of a user meeting a preset condition, wherein the user information comprises basic information, behavior information and remote information;
generating a plurality of local feature parameters of the user based on the behavior information;
generating a plurality of remote characteristic parameters of the user based on the remote information;
comparing the local characteristic parameters, the remote characteristic parameters and the preset strategies to determine a target strategy;
and adjusting the current user policy of the user based on the target policy.
2. The method of claim 1, further comprising:
acquiring user information of a plurality of historical users;
and generating the preset strategies based on the user information of the historical users.
3. The method of claim 2, wherein generating the plurality of preset policies based on the user information of the plurality of historical users comprises:
behavior information in the user information of the plurality of historical users is obtained;
determining time ranges in the plurality of preset strategies based on time data in the behavior information.
4. The method of claim 2, wherein generating the plurality of preset policies based on the user information of the plurality of historical users comprises:
acquiring remote information in the user information of the plurality of historical users;
and determining the service characteristics in the preset strategies based on the time data in the remote information.
5. The method of claim 2, wherein generating the plurality of preset policies based on the user information of the plurality of historical users comprises:
acquiring basic information in the user information of the plurality of historical users;
and generating a blacklist in the plurality of preset strategies based on the basic information.
6. The method of claim 1, wherein comparing the plurality of local feature parameters, the plurality of remote feature parameters, and the plurality of predetermined policies to determine a target policy comprises:
and when the local characteristic parameters of the user are within the time range in the preset strategy, the remote characteristic parameters do not contain the service characteristics in the preset strategy, and no preset behavior exists, allocating a first target strategy for the user.
7. The method of claim 1, wherein comparing the plurality of local feature parameters, the plurality of remote feature parameters, and the plurality of predetermined policies to determine a target policy comprises:
and when the local characteristic parameters of the user are within the time range in the preset strategy and the remote characteristic parameters do not contain the service characteristics in the preset strategy and contain the preset behavior, distributing a second target strategy for the user.
8. The method of claim 1, wherein comparing the plurality of local feature parameters, the plurality of remote feature parameters, and the plurality of predetermined policies to determine a target policy comprises:
when the local characteristic parameters of the user are not in a time range in a preset strategy, and the remote characteristic parameters do not contain service characteristics in the preset strategy and are not in a blacklist, extracting historical information of the user;
and when the historical information meets a preset condition, distributing a third target strategy for the user.
9. The method of claim 1, wherein adjusting the user's current user policy based on the target policy comprises:
adjusting the current quota of the user based on the target strategy;
adjusting the current period of the user based on the target strategy;
and adjusting the current special shared resource information of the user based on the target strategy.
10. A user policy adjustment apparatus, comprising:
the information module is used for acquiring user information of a user meeting preset conditions, wherein the user information comprises basic information, behavior information and remote information;
a local module for generating a plurality of local feature parameters of the user based on the behavior information;
a remote module for generating a plurality of remote characteristic parameters of the user based on the remote information;
the strategy module is used for comparing the local characteristic parameters, the remote characteristic parameters and the preset strategies to determine a target strategy;
and the adjusting module is used for adjusting the current user strategy of the user based on the target strategy.
11. An electronic device, comprising:
one or more processors;
storage means for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-9.
12. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-9.
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