CN113570166A - Wind control real-time prediction identification method and device - Google Patents
Wind control real-time prediction identification method and device Download PDFInfo
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Abstract
The invention provides a method and a device for identifying wind control real-time prediction, wherein the method comprises the steps of logging in a matched drools rule engine by a client, collecting basic information data and sending the basic information data to a data storage server in real time; the data storage server stores the data to mongodb; the service server collects real-time behavior information data and sends the real-time behavior information data to the wind control server redis for storage; the wind control server reads the redis behavior data and the mongodb original data to assemble a List data set, and a user risk label is set; and the wind control server decides the user according to the risk label of the user, calculates the score by using the hypothetical person scoring card model, and sets a platform punishment mechanism so as to facilitate manual intervention and platform maintenance. Compared with the prior art, the method for identifying the wind control real-time prediction provided by the invention has the advantages of high speed of identifying the risk users, high data processing speed, rule matching, risk verification and real-time processing of user behavior data.
Description
Technical Field
The invention relates to the technical field of networks, in particular to a method and a device for real-time prediction and identification of wind control.
Background
And the wind control real-time prediction is used for each link and operation of the e-commerce system. Whether the user has risks or not is mainly identified and predicted, measures such as risk prompt, blocking and permission limitation are taken, all functions of the platform are guaranteed to be used by the user safely, and property safety of the user on the platform is maintained. However, in the actual use process, most of the wind controls adopt offline calculation or T +1 calculation, and after a user is cheated, the platform finds that the account has risks and abnormalities.
Therefore, there is a need to provide a novel method and apparatus for identifying wind-controlled real-time prediction, so as to overcome the above-mentioned drawbacks.
Disclosure of Invention
The invention aims to provide a novel method and a novel device for identifying wind control real-time prediction, which are high in speed of identifying risk users, high in data processing speed, rule matching, risk verification and real-time processing of user behavior data.
In order to achieve the above object, the present invention provides a method for identifying a wind-control real-time prediction, comprising:
the client logs in a matched drools rule engine, inquires and returns partial function authority, and acquires basic information data and sends the basic information data to a data storage server in real time;
the data storage server stores the data to the mongodb wide table as original data;
the service server collects real-time behavior information data and sends the data to the wind control server redis for storage in real time;
the wind control server reads the redis behavior data and the mongodb original data to assemble a List data set, matches a drools rule engine in real time and sets a user risk label;
and the wind control server decides the users according to the risk labels of the users, calculates the values by utilizing the hypothetical person scoring card model, and sets a platform punishment mechanism according to the calculated values so as to facilitate manual intervention and platform maintenance.
Further, the data storage server storing the data to the mongodb wide table as the original data comprises:
after the data storage server receives the data, the json deserializes the data to analyze the required field data;
and storing the data subjected to the deserialization solution into a mongodb wide table as original data for inquiring when a follow-up rule engine is matched.
Further, the service server collects real-time behavior information data, and sends the data to the wind control server redis in real time for storage, wherein the data comprises:
the method comprises the steps that a wind control server receives kafka/rocktmq message data, and stores user behavior aging data and data required by rules to redis;
and (4) reserving hot spot data by utilizing a redis LRU algorithm, calculating and matching a rule once when a user operates a service behavior once, and updating the operation authority of the user in real time.
Further, the wind control server reads the redis behavior data and the mongodb original data, assembles the redis behavior data and the mongodb original data into a List data set, matches the drools rule engine in real time, and sets a user risk tag, including:
the wind control server aggregates different data of the user according to the behavior of the user, reads the redis behavior data and the mongodb original data to assemble a List data set, matches the drools rule engine in real time, sets a risk label corresponding to the user according to the rule, and stores the risk label to mysql.
Further, the wind control server decides the user according to the risk label of the user, and the calculating of the score by using the hypothetical people scoring card model comprises:
and the wind control back-end server decides the user according to the risk label of the user, inquires all labels of the user to cross match all the hypothetical person scoring card models to calculate the score, judges the hypothetical person to which the user belongs according to the matching degree and the score, sets the identity of the hypothetical person of the user and stores the identity to mysql.
The invention also provides a wind control real-time prediction and identification device, which applies the steps of the wind control real-time prediction and identification method and comprises a client, a business layer, a data layer, a rule engine and a result layer;
the client is used for logging in and querying a user and acquiring information and is matched with a drools rule engine;
the business layer is used for responding the business of the client and acquiring behavior information data in real time;
the data layer is used for storing basic information data acquired by the client and storing real-time behavior data acquired by the service layer;
the rule engine is used for performing real-time calculation and rule matching according to the set rule and updating the user operation authority in real time;
and the result layer is used for controlling the operation authority of the user and corresponding to the contact mechanism, and adjusting the wind control strategy in real time.
Further, the client comprises an APP, a PC, an M station or an applet; the business layer comprises IM, supply, purchase, search and transaction; the data layer comprises a Redis memory and a Mongodb memory; the rules engine includes entitlement rules, label rules, decision rules, and other rules.
The present invention also provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the computer program implements the steps of the wind-control real-time prediction identification method.
The invention also provides a computer terminal which comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, wherein the processor realizes the steps of the wind control real-time prediction identification method when executing the computer program.
Compared with the related technology, the method and the device for real-time prediction and identification of wind control adopt the real-time collection of user behavior data and non-offline calculation; a real-time matching rules engine; the real-time calculation decision engine stores hot data, eliminates invalid data release space, has high speed of identifying risk users, and blocks safety risk before users use various rights and interests; the method comprises the following steps of fast data processing, rule matching, risk verification and real-time processing of user behavior data; the rules are updated and deployed in a hot mode, the platform can adjust all the rules of the rule engine at any time and take effect immediately without re-issuing service; the data capacity is large, and the data storage container is transversely expanded.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts, wherein:
FIG. 1 is a flow chart of a method for identifying a wind-controlled real-time forecast according to the present invention;
fig. 2 is an architecture diagram of the wind-control real-time prediction and recognition device of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the present invention provides a method for identifying a wind-controlled real-time prediction, including:
s1, logging in a matched drools rule engine by the client, inquiring and returning partial function authority, and acquiring basic information data and sending the basic information data to the data storage server in real time;
and (3) registering and logging by a user, matching a drools rule engine through information of client equipment, ip, awakening places and the like, and inquiring and returning partial function authority of the user. (examples of the drools rule engine include limiting the number of chats for users registered in the ip of the risk area, limiting the number of transactions for users registered in the risk app device number, and the like).
And acquiring basic information data, collecting various information (information such as base information, authentication, members, identities and the like) of the user, and sending the information to a back-end data storage server in real time through kafka/rocktmq.
And S2, the data storage server stores the data into the mongodb wide table as the original data.
Basic information data are stored to mongodb, after the data are received by the back-end data storage server, field data needed by data json deserialization analysis are stored to a mongodb broad table to serve as original data for follow-up query during rule engine matching, and the method has the advantages that various data of a user can be rapidly queried during high concurrency, and rule matching of the rule engine is facilitated.
And S3, the service server collects real-time behavior information data and sends the data to the wind control server redis for storage in real time.
And (3) acquiring real-time behavior information data, and transmitting the data to a wind control back-end server in real time by a service line back-end server (such as IM, supply, purchase and transaction) through kafka/rocktmq.
The real-time behavior information data is stored in a redis, the wind control back-end server receives kafka/rocktmq message data, user behavior aging data and data required by rules are stored in the redis, hot spot data are reserved by using a redis LRU algorithm, once business behavior is operated by a user, the rules are calculated and matched in real time, and the operation authority of the user is updated in real time.
And S4, the wind control server reads the redis behavior data and the mongodb original data to assemble a List data set, matches the drools rule engine in real time, and sets a user risk label.
Setting a user risk label, aggregating different data of a user by a wind control back-end server according to the behavior of the user, reading redis behavior data and mongodb original data to assemble a List data set, matching a drools rule engine in real time, setting a risk label corresponding to the user according to the rule, and storing the risk label to mysql. (examples of drools rules Engine: areas of risk and categories of risk for released supply; false quotes for areas of purchase release, etc.)
S5, the wind control server decides the user according to the risk label of the user, calculates the score by using the hypothetical people scoring card model, and sets a platform punishment mechanism according to the calculated score so as to facilitate manual intervention and platform maintenance.
And (3) deciding the user, wherein the wind control back-end server decides the user according to the risk label of the user, inquires all labels of the user to be matched with all the hypothetical person scoring card models in a cross mode to calculate the score (the hypothetical person scoring card model is composed of a plurality of labels with different scores and is configured by operation according to specific conditions at the background), judges the hypothetical person to which the user belongs according to the matching degree and the score, sets the identity of the hypothetical person of the user and stores the identity to mysql.
And the operation authority sets a platform punishment mechanism according to the identity of the hypothetical person, controls the user operation authority and a corresponding release mechanism, inquires the operation authority of the user during registration and login of the user or other operations, and blocks the user.
And (3) background manual intervention, wherein operators find that the risk labels and the identity of the hypothetical person of some users are abnormal, and can manually intervene to adjust the labels and the identity of the hypothetical person in the operation background, such as prolonging the punishment period, modifying the hypothetical person, modifying the operation authority and the like, and catch the missed fishes of the real-time system.
And background maintenance, adding operations of adding, deleting, modifying and checking data such as label rules and decision rules in an operation background, updating a drools rule engine in a hot mode, and immediately taking effect after modification, wherein an operator can adjust the platform wind control strategy in real time.
Referring to fig. 2, the present invention further provides a wind control real-time prediction and identification device, which applies the steps of the wind control real-time prediction and identification method described above and includes a client, a service layer, a data layer, a rule engine, and a result layer;
the client is used for logging in and querying a user and acquiring information and is matched with a drools rule engine;
the business layer is used for responding the business of the client and acquiring behavior information data in real time;
the data layer is used for storing basic information data acquired by the client and storing real-time behavior data acquired by the service layer;
the rule engine is used for performing real-time calculation and rule matching according to the set rule and updating the user operation authority in real time;
and the result layer is used for controlling the operation authority of the user and corresponding to the contact mechanism, and adjusting the wind control strategy in real time.
Further, the client comprises an APP, a PC, an M station or an applet; the business layer comprises IM, supply, purchase, search and transaction; the data layer comprises a Redis memory and a Mongodb memory; the rules engine includes entitlement rules, label rules, decision rules, and other rules.
The present invention also provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the computer program implements the steps of the wind-control real-time prediction identification method.
The invention also provides a computer terminal which comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, wherein the processor realizes the steps of the wind control real-time prediction identification method when executing the computer program.
The processor, when executing the computer program, implements the functions of the modules/units in the above-described device embodiments. Illustratively, the computer program may be partitioned into one or more modules/units that are stored in the memory and executed by the processor to implement the invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program in the terminal device.
The computer terminal can be a desktop computer, a notebook, a palm computer, a cloud server and other computing equipment. May include, but is not limited to, a processor, memory. More or fewer components may be included, or certain components may be combined, or different components may be included, such as input-output devices, network access devices, buses, and so forth.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The storage may be an internal storage unit, such as a hard disk or a memory. The memory may also be an external storage device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), or the like. Further, the memory may also include both an internal storage unit and an external storage device. The memory is used for storing the computer program and other programs and data. The memory may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other ways. For example, the above-described embodiments of the apparatus/terminal device are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (9)
1. A method for identifying wind control real-time prediction is characterized by comprising the following steps:
the client logs in a matched drools rule engine, inquires and returns partial function authority, and acquires basic information data and sends the basic information data to a data storage server in real time;
the data storage server stores the data to the mongodb wide table as original data;
the service server collects real-time behavior information data and sends the data to the wind control server redis for storage in real time;
the wind control server reads the redis behavior data and the mongodb original data to assemble a List data set, matches a drools rule engine in real time and sets a user risk label;
and the wind control server decides the users according to the risk labels of the users, calculates the values by utilizing the hypothetical person scoring card model, and sets a platform punishment mechanism according to the calculated values so as to facilitate manual intervention and platform maintenance.
2. The method for identifying wind-controlled real-time prediction as claimed in claim 1, wherein the step of storing the data into the mongodb wide table by the data storage server as raw data comprises the following steps:
after the data storage server receives the data, the json deserializes the data to analyze the required field data;
and storing the data subjected to the deserialization solution into a mongodb wide table as original data for inquiring when a follow-up rule engine is matched.
3. The method for real-time wind control prediction and identification according to claim 1, wherein the step of collecting real-time behavior information data by the service server and sending the data to the wind control server for redis storage in real time comprises the steps of:
the method comprises the steps that a wind control server receives kafka/rocktmq message data, and stores user behavior aging data and data required by rules to redis;
and (4) reserving hot spot data by utilizing a redis LRU algorithm, calculating and matching a rule once when a user operates a service behavior once, and updating the operation authority of the user in real time.
4. The method for identifying wind-control real-time prediction according to claim 1, wherein the wind-control server reads redis behavior data and mongodb raw data, assembles the redis behavior data and the mongodb raw data into a List data set, matches a drools rule engine in real time, and setting a user risk label comprises:
the wind control server aggregates different data of the user according to the behavior of the user, reads the redis behavior data and the mongodb original data to assemble a List data set, matches the drools rule engine in real time, sets a risk label corresponding to the user according to the rule, and stores the risk label to mysql.
5. The method for real-time wind-control forecast identification according to claim 1, wherein said wind-control server decides users according to their risk labels, and calculating scores using a hypothetical scoring card model comprises:
and the wind control back-end server decides the user according to the risk label of the user, inquires all labels of the user to cross match all the hypothetical person scoring card models to calculate the score, judges the hypothetical person to which the user belongs according to the matching degree and the score, sets the identity of the hypothetical person of the user and stores the identity to mysql.
6. A wind control real-time prediction and identification device, which is characterized in that the device applies the steps of the wind control real-time prediction and identification method of any one of the claims 1 to 5, and comprises a client, a business layer, a data layer, a rule engine and a result layer;
the client is used for logging in and querying a user and acquiring information and is matched with a drools rule engine;
the business layer is used for responding the business of the client and acquiring behavior information data in real time;
the data layer is used for storing basic information data acquired by the client and storing real-time behavior data acquired by the service layer;
the rule engine is used for performing real-time calculation and rule matching according to the set rule and updating the user operation authority in real time;
and the result layer is used for controlling the operation authority of the user and corresponding to the contact mechanism, and adjusting the wind control strategy in real time.
7. The device of claim 6, wherein the client comprises an APP, a PC, an M-station, or an applet; the business layer comprises IM, supply, purchase, search and transaction; the data layer comprises a Redis memory and a Mongodb memory; the rules engine includes entitlement rules, label rules, decision rules, and other rules.
8. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program, which when executed by a processor implements the steps of the method for identifying a wind-controlled real-time prediction according to any one of the preceding claims 1 to 5.
9. A computer terminal, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method for identifying a wind-controlled real-time forecast according to any one of the preceding claims 1-5 when executing the computer program.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101714273A (en) * | 2009-05-26 | 2010-05-26 | 北京银丰新融科技开发有限公司 | Rule engine-based method and system for monitoring exceptional service of bank |
| US20130104236A1 (en) * | 2011-10-14 | 2013-04-25 | Albeado, Inc. | Pervasive, domain and situational-aware, adaptive, automated, and coordinated analysis and control of enterprise-wide computers, networks, and applications for mitigation of business and operational risks and enhancement of cyber security |
| US20160119195A1 (en) * | 2014-10-23 | 2016-04-28 | International Business Machines Corporation | Computing service level risk |
| CN106250408A (en) * | 2016-07-21 | 2016-12-21 | 湖南惠农科技有限公司 | Network address access method and device |
| CN108875388A (en) * | 2018-05-31 | 2018-11-23 | 康键信息技术（深圳）有限公司 | Real-time risk control method, device and computer readable storage medium |
| CN110347568A (en) * | 2019-06-27 | 2019-10-18 | 苏州浪潮智能科技有限公司 | The treating method and apparatus of user behavior data |
| WO2021031607A1 (en) * | 2019-08-22 | 2021-02-25 | 上海哔哩哔哩科技有限公司 | Risk control method, computer device, and readable storage medium |
| WO2021042843A1 (en) * | 2019-09-06 | 2021-03-11 | 平安科技（深圳）有限公司 | Alert information decision method and apparatus, computer device and storage medium |
| CN112700329A (en) * | 2021-01-27 | 2021-04-23 | 永辉云金科技有限公司 | Response method of wind control rule engine and wind control rule engine |
| CN112765514A (en) * | 2019-11-06 | 2021-05-07 | 腾讯科技（深圳）有限公司 | Method, device and storage medium for monitoring network public sentiment |
| CN113032764A (en) * | 2021-03-24 | 2021-06-25 | 北京顶象技术有限公司 | Account registration login service wind control system and service wind control method |
| CN113159637A (en) * | 2021-05-14 | 2021-07-23 | 中国建设银行股份有限公司 | Malicious user determination method and device, storage medium and electronic device |
| CN113641758A (en) * | 2021-08-11 | 2021-11-12 | 广州宸祺出行科技有限公司 | Wind control statistical method and device based on real-time warehouse data acquisition |
-
2021
- 2021-09-08 CN CN202111049666.2A patent/CN113570166A/en active Pending
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101714273A (en) * | 2009-05-26 | 2010-05-26 | 北京银丰新融科技开发有限公司 | Rule engine-based method and system for monitoring exceptional service of bank |
| US20130104236A1 (en) * | 2011-10-14 | 2013-04-25 | Albeado, Inc. | Pervasive, domain and situational-aware, adaptive, automated, and coordinated analysis and control of enterprise-wide computers, networks, and applications for mitigation of business and operational risks and enhancement of cyber security |
| US20160119195A1 (en) * | 2014-10-23 | 2016-04-28 | International Business Machines Corporation | Computing service level risk |
| CN106250408A (en) * | 2016-07-21 | 2016-12-21 | 湖南惠农科技有限公司 | Network address access method and device |
| CN108875388A (en) * | 2018-05-31 | 2018-11-23 | 康键信息技术（深圳）有限公司 | Real-time risk control method, device and computer readable storage medium |
| CN110347568A (en) * | 2019-06-27 | 2019-10-18 | 苏州浪潮智能科技有限公司 | The treating method and apparatus of user behavior data |
| WO2021031607A1 (en) * | 2019-08-22 | 2021-02-25 | 上海哔哩哔哩科技有限公司 | Risk control method, computer device, and readable storage medium |
| WO2021042843A1 (en) * | 2019-09-06 | 2021-03-11 | 平安科技（深圳）有限公司 | Alert information decision method and apparatus, computer device and storage medium |
| CN112765514A (en) * | 2019-11-06 | 2021-05-07 | 腾讯科技（深圳）有限公司 | Method, device and storage medium for monitoring network public sentiment |
| CN112700329A (en) * | 2021-01-27 | 2021-04-23 | 永辉云金科技有限公司 | Response method of wind control rule engine and wind control rule engine |
| CN113032764A (en) * | 2021-03-24 | 2021-06-25 | 北京顶象技术有限公司 | Account registration login service wind control system and service wind control method |
| CN113159637A (en) * | 2021-05-14 | 2021-07-23 | 中国建设银行股份有限公司 | Malicious user determination method and device, storage medium and electronic device |
| CN113641758A (en) * | 2021-08-11 | 2021-11-12 | 广州宸祺出行科技有限公司 | Wind control statistical method and device based on real-time warehouse data acquisition |
Cited By (1)
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| CN115730283A (en) * | 2022-10-19 | 2023-03-03 | 广州易幻网络科技有限公司 | Account login wind control system and method, computer equipment and storage medium |
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