CN112685610A

CN112685610A - False registration account identification method and related device

Info

Publication number: CN112685610A
Application number: CN202011552175.5A
Authority: CN
Inventors: 孙家棣; 马宁
Original assignee: Ping An Life Insurance Company of China Ltd
Current assignee: Ping An Life Insurance Company of China Ltd
Priority date: 2020-12-24
Filing date: 2020-12-24
Publication date: 2021-04-20
Anticipated expiration: 2040-12-24
Also published as: CN112685610B

Abstract

The application provides a false registration account identification method and a related device, and belongs to the technical field of big data. The method comprises the following steps: acquiring first account data of an account set to be identified and second account data of a white list account set; traversing and combining the equipment parameter variable fields to obtain an equipment parameter variable field group; acquiring a first field data group corresponding to the equipment parameter variable field group from the first account data and acquiring a corresponding second field data group from the second account data; acquiring the number proportion of accounts to be identified corresponding to the first field data group in the account set to be identified, and acquiring the number proportion of white list accounts corresponding to the second field data group in the white list account set; and comparing the number ratio of the accounts to be identified with the number ratio of the accounts in the white list to obtain the false registration account. The application also relates to the field of blockchains, and the second account data can be stored in the blockchain. The coverage rate and the overall identification accuracy of the false registration account identification are effectively improved.

Description

False registration account identification method and related device

Technical Field

The application relates to the technical field of big data, in particular to a false registration account identification method and a related device.

Background

The black industry of the internet is closely related to the life of people, and a large number of false account numbers are usually registered in black products, and the making of profit by pulling wool brings great adverse effects to normal industries, so that the identification and the cleaning of the registered false account numbers are very important.

In terms of identifying and fighting false accounts, the existing method is a wind control rule forming strategy, namely, accounts hitting wind control rules are considered as false accounts, and the wind control rules are generally summarized according to expert experience, such as simulator operation, sdk data analysis exception, sandbox virtual environment operation and the like. The wind control rule forming strategy has the advantages of being fast on-line, easy to explain, accurate in single-point striking, but has the weaknesses that the strategy is easily bypassed by a fake account number of a black product, the coverage rate is low, and the overall identification accuracy is low.

It is to be noted that the information disclosed in the above background section is only for enhancement of understanding of the background of the present disclosure, and thus may include information that does not constitute prior art known to those of ordinary skill in the art.

Disclosure of Invention

The embodiment of the application aims to provide a false registration account identification method and device, which can effectively improve the coverage rate and the overall identification accuracy of false registration account identification.

According to one embodiment of the application, a false registration account identification method comprises the following steps: acquiring first account data corresponding to an account set to be identified and second account data corresponding to a white list account set, wherein the first account data and the second account data both comprise field data of equipment parameter variable fields;

traversing and combining the equipment parameter variable fields to obtain a plurality of equipment parameter variable field groups;

respectively acquiring first field data groups corresponding to the equipment parameter variable field groups from the first account data, and respectively acquiring second field data groups corresponding to the equipment parameter variable field groups from the second account data;

acquiring the number proportion of the accounts to be identified corresponding to the first field data group in the account set to be identified, and acquiring the number proportion of the white list accounts corresponding to the second field data group in the white list account set;

and comparing the number proportion of the account numbers to be identified corresponding to each equipment parameter variable field group with the number proportion of the account numbers in the white list, determining the abnormal equipment parameter variable field group, and determining the account numbers to be identified corresponding to the abnormal equipment parameter variable field group as false registered account numbers.

In some embodiments of the present application, the traversing and combining the device parameter variable fields to obtain a plurality of device parameter variable field groups includes:

and carrying out full-free quantity traversal combination on the equipment parameter variable fields to obtain a plurality of equipment parameter variable field groups.

acquiring each equipment parameter variable field with the number of field data smaller than a preset number from the first account data as a field to be aggregated, and taking each residual equipment parameter variable field as a first field to be combined;

aggregating the fields to be aggregated to obtain a plurality of second fields to be combined;

and traversing and combining all the first fields to be combined and the second fields to be combined to obtain a plurality of equipment parameter variable field groups.

In some embodiments of the present application, the traversing and combining all the first fields to be combined and the second fields to be combined to obtain a plurality of field sets of device parameter variables includes:

and randomly acquiring fields with the preset number of fields from all the fields to be combined and the fields to be combined to obtain a plurality of field groups of the equipment parameter variables.

In some embodiments of the present application, the aggregating the fields to be aggregated to obtain a plurality of second fields to be combined includes:

and according to a preset field correlation standard, aggregating the related fields to be aggregated to obtain a plurality of second fields to be combined.

and randomly aggregating the fields to be aggregated to obtain a plurality of second fields to be combined, so that the field number of all the first fields to be combined and the second fields to be combined is less than or equal to a specific number.

In some embodiments of the present application, the acquiring first account data corresponding to an account set to be identified includes:

acquiring account data corresponding to a target account set, wherein the account data comprises field data of an environment variable field and field data of a device parameter variable field;

acquiring a target account number of the same field data of the environmental variable field in a target account number set;

acquiring the number of account numbers of target account numbers of the same field data related to the environment variable field;

determining the abnormal target account number based on the number of the account numbers, and acquiring field data of a device parameter variable field corresponding to the abnormal target account number as first account number data corresponding to the account number set to be identified.

In some embodiments of the present application, the determining the abnormal target account number based on the number of account numbers includes:

and when the number of the account numbers is larger than or equal to the preset number of the account numbers, determining the target account number of the same field data related to the environment variable field as the abnormal target account number.

According to another embodiment of the present application, a false registered account identification apparatus includes:

the system comprises a first acquisition module, a second acquisition module and a processing module, wherein the first acquisition module is used for acquiring first account data corresponding to an account set to be identified and acquiring second account data corresponding to a white list account set, and the first account data and the second account data both comprise field data of equipment parameter variable fields;

the combination module is used for performing traversal combination on the equipment parameter variable fields to obtain a plurality of equipment parameter variable field groups;

a second obtaining module, configured to obtain first field data groups corresponding to the device parameter variable field groups from the first account data, and obtain second field data groups corresponding to the device parameter variable field groups from the second account data;

a third obtaining module, configured to obtain a number proportion of the account to be identified, corresponding to the account set to be identified, of the first field data group, and obtain a number proportion of the white list account, corresponding to the white list account set, of the second field data group;

the determining module is used for comparing the number proportion of the accounts to be identified corresponding to each equipment parameter variable field group with the number proportion of the accounts in the white list, determining the abnormal equipment parameter variable field group, and determining the accounts to be identified corresponding to the abnormal equipment parameter variable field group as false registered accounts.

According to another embodiment of the present application, an electronic device may include: a memory storing computer readable instructions; a processor reading computer readable instructions stored by the memory to perform the method as described above.

According to another embodiment of the present application, a computer program medium having computer readable instructions stored thereon, which, when executed by a processor of a computer, cause the computer to perform the method as described above.

According to the embodiment of the application, first account data corresponding to an account set to be identified is obtained, second account data corresponding to a white list account set is obtained, and the first account data and the second account data both comprise field data of equipment parameter variable fields; then, traversing and combining the equipment parameter variable fields to obtain a plurality of equipment parameter variable field groups; respectively acquiring a first field data group corresponding to a device parameter variable field group from the first account data, and respectively acquiring a second field data group corresponding to the device parameter variable field group from the second account data; then, acquiring the number proportion of the accounts to be identified corresponding to the first field data group in the account set to be identified, and acquiring the number proportion of the white list accounts corresponding to the second field data group in the white list account set; and finally, comparing the number proportion of the accounts to be identified corresponding to each equipment parameter variable field group with the number proportion of the accounts in the white list, determining abnormal equipment parameter variable field groups, and determining the accounts to be identified corresponding to the abnormal equipment parameter variable field groups as false registered accounts.

Furthermore, a plurality of equipment parameter variable field groups are obtained by traversing and combining the equipment parameter variable fields, so that the condition of large aggregation of accounts caused by popular equipment parameter variable fields is avoided, the collected equipment parameters can be subjected to full statistics, the defect that only a few parameters can be used for identification in the related technology is avoided, the usable data information of the wind control can be fully utilized, and the identification coverage rate is effectively improved; the guiding accuracy of the equipment parameters to the false account aggregation is improved through the equipment parameter variable field group, the robustness to the black product variation is strong, and the black product cannot be bypassed easily; the overall identification accuracy of the false account is effectively improved.

Other features and advantages of the present application will be apparent from the following detailed description, taken in conjunction with the accompanying drawings, or may be learned by practice of the application.

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 application.

Drawings

FIG. 1 shows a schematic diagram of a system to which embodiments of the present application may be applied.

Fig. 2 shows a flowchart of a false registered account identification method according to an embodiment of the present application.

Fig. 3 shows a flow chart of a false registered account identification method according to yet another embodiment of the present application.

Fig. 4 shows a block diagram of a false registered account identification apparatus according to an embodiment of the present application.

FIG. 5 shows a block diagram of an electronic device according to an embodiment of the application.

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 examples 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.

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 application. One skilled in the relevant art will recognize, however, that the subject matter of the present application 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 application.

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.

FIG. 1 shows a schematic diagram of a system 100 to which embodiments of the present application may be applied.

As shown in fig. 1, the system 100 may include a server 101 and a terminal 102. The terminal 102 and the server 101 may be directly or indirectly connected through wireless communication, and the application is not limited in this respect. The server 101 may collect device parameters and environment variable parameters of the account registered on the terminal 102. The server 101 may be a cloud server. The server 101 may also be a node in the blockchain network, and is configured to identify a false registration account, and the server 101 may share the identification result with the blockchain network where the server is located, and obtain related data of the shared white list account on the blockchain.

In an embodiment of this example, as shown in fig. 1, a server 101 obtains first account data corresponding to an account set to be identified, and obtains second account data corresponding to a white list account set, where the first account data and the second account data both include field data of a device parameter variable field; then, traversing and combining the equipment parameter variable fields to obtain a plurality of equipment parameter variable field groups; respectively acquiring a first field data group corresponding to a device parameter variable field group from the first account data, and respectively acquiring a second field data group corresponding to the device parameter variable field group from the second account data; then, acquiring the number proportion of the accounts to be identified corresponding to the first field data group in the account set to be identified, and acquiring the number proportion of the white list accounts corresponding to the second field data group in the white list account set; and finally, comparing the number ratio of the accounts to be identified with the number ratio of the accounts in the white list, determining an abnormal first field data group, and determining the accounts to be identified corresponding to the first field data group as false registered accounts.

Fig. 2 schematically shows a flowchart of a false registered account identification method according to an embodiment of the present application. The execution subject of the false registered account identification method may be an electronic device having a calculation processing function, such as the server 101 or the terminal 102 shown in fig. 1.

As shown in fig. 2, the false registered account identification method may include steps S210 to S250.

Step S210, acquiring first account data corresponding to an account set to be identified and acquiring second account data corresponding to a white list account set, wherein the first account data and the second account data both comprise field data of equipment parameter variable fields;

step S220, traversing and combining the equipment parameter variable fields to obtain a plurality of equipment parameter variable field groups;

step S230, respectively obtaining a first field data group corresponding to each device parameter variable field group from the first account data, and respectively obtaining a second field data group corresponding to each device parameter variable field group from the second account data;

step S240, obtaining the number ratio of the account to be identified corresponding to each first field data group in the account set to be identified, and obtaining the number ratio of the white list account corresponding to each second field data group in the white list account set;

step S250, comparing the number of the accounts to be identified corresponding to each of the device parameter variable field groups with the number of the accounts in the white list, determining the abnormal device parameter variable field group, and determining the account to be identified corresponding to the abnormal device parameter variable field group as a false registered account.

The following describes specific processes of each step performed when the false registered account is identified.

In step S210, first account data corresponding to an account set to be identified is obtained, and second account data corresponding to a white list account set is obtained, where the first account data and the second account data both include field data of a device parameter variable field.

In this exemplary embodiment, the account set to be identified is used to identify false accounts contained therein, and may be a batch of accounts collected by a certain enterprise or platform. And the first account data corresponding to the account set to be identified is account related attribute data of the account to be identified.

The white list account set is a collection of collected normal accounts, and can be account related attribute data collected by a tool kit when account registration is performed by a certain company, such as an employee in the company, an agent, a user purchasing a policy, and the like.

The device parameter variable field is related parameters of the device of which the registered account is, such as an installation system, a device model, the number of applications, and the like. The account number opposite to the device parameter variable field typically also includes field data of an environment variable field, such as a device address, a wireless network address, location information, and the like.

In an embodiment, referring to fig. 3, acquiring first account data corresponding to an account set to be identified includes:

step S310, account data corresponding to a target account set is obtained, wherein the account data comprises field data of an environment variable field and field data of a device parameter variable field;

step S320, acquiring a target account which is concentrated and is related to the target account of the same field data of the environment variable field;

step S330, acquiring the number of the account numbers of the target account numbers of the same field data related to the environment variable field;

step S340, determining the abnormal target account based on the number of the accounts, and acquiring field data of a device parameter variable field corresponding to the abnormal target account as first account data corresponding to the account set to be identified.

In the embodiment of the present example, the set of accounts to be identified is a set of fields to be identified with false account suspicion, which are verified in advance through the field of the environment variable. The environment variable field device address, wireless network address, location information, etc.

The target account number set may be all the account numbers collected by the verification task of a certain platform. Acquiring target accounts, which are concentrated in target accounts and are associated with the same field data of the environment variable field, for example, acquiring target accounts, which are concentrated in target accounts and are all associated with wireless network address field (wireless network address field data), and then, when the number of the target accounts is large (for example, exceeds the preset number of the accounts), indicating that obvious aggregation exists to a certain extent, and further false registered account suspicion exists. Furthermore, an abnormal target account number can be determined based on the number of account numbers, and then the abnormal target account number is used as the account number set to be identified.

In one embodiment, determining the abnormal target account number based on the number of account numbers includes:

The number of the preset account numbers can be set according to actual conditions. When the number of the account numbers of the target account numbers of the same field data related to the environment variable field is larger than or equal to the preset number of the account numbers, the fact that the account numbers of the same field data related to the environment variable field are aggregated is proved to be suspected.

In step S220, traversing and combining the device parameter variable fields to obtain a plurality of device parameter variable field groups.

In the embodiment of the present example, traversing and combining fields is to combine some fields together to form a field group. Because the device parameter variable field usually includes popular fields, such as system and device model, and field data corresponding to these popular fields usually has a large number of account numbers associated therewith, for example, the android system has a large number of account numbers associated therewith; the device parameter variables also include personalized fields, such as the number of applications, which are typically installed on different devices, and thus fewer accounts are associated with the number of applications. When the accounts are registered, if the accounts are normal accounts, data corresponding to the personalized equipment parameter variable fields among the accounts are not similar in a large amount, and data corresponding to popular equipment parameter variable fields are likely to be similar in a large amount.

Through traversing and combining the equipment parameter variable fields, a plurality of equipment parameter variable field groups are obtained, and subsequent analysis can effectively avoid mass aggregation of accounts caused by popular equipment parameter variable fields and misjudge the accounts as batch aggregated false accounts registered by black products.

In one embodiment, performing traversal combination on the device parameter variable fields to obtain a plurality of device parameter variable field sets includes:

The number of the device parameter variable fields is usually large, for example, the number of the device parameter variable fields is usually more than 100, full-free-quantity traversal combination refers to free combination of fields corresponding to each account, and the calculation quantity of the traversal combination at this time

When the number of the equipment parameter variable fields exceeds 100, the calculation amount of the full-free traversal combination is

Where n is a field. The full-free traversal combination is more computationally intensive, but can be applied to various field combination cases.

The number of field data contained is less than the predetermined number of device parameter variable fields, that is, the number of field data corresponding to a certain device parameter variable field is less than the predetermined number, for example, the field data of this field of the device system is only 2, android and ios. The field data for this field of device model may be hundreds, such as Huaye, millet, red rice, vivo, apple, etc. Further, when the predetermined number is 5, the device system field may be acquired as the field to be aggregated, and the remaining, e.g., device model field may be taken as the first field to be combined.

And aggregating the fields to be aggregated to obtain a plurality of second fields to be combined, namely combining the fields to be aggregated together to form one field. The aggregating of the fields to be aggregated may be to randomly obtain a predetermined number of fields to be aggregated and combine them together, or to aggregate some fields to be aggregated having correlation.

Therefore, all the fields to be combined and the fields to be combined are subjected to traversal combination, when a plurality of equipment parameter variable field groups are obtained, because combination is performed in advance, the number of the total fields is reduced, and the calculation amount of traversal combination is effectively reduced.

In one embodiment, traversing and combining all the first fields to be combined and the second fields to be combined to obtain a plurality of field sets of device parameter variables includes:

The number of the predetermined fields can be set according to the precision requirement, for example, the number of the predetermined fields is 3; by acquiring and combining fields with the preset number of fields and combining fields to be aggregated in advance in the embodiment, the inventor finds that only the fields with the preset number of fields are acquired and combined, and then can accurately judge abnormal field combinations in subsequent steps to determine the false account.

Meanwhile, the method for avoiding full-free quantity traversal combination is to calculate more redundant calculation quantity when the flow quantity is calculated under the condition of full-quantity equipment parameter combination.

In one embodiment, the predetermined number of fields is greater than or equal to 1 and less than or equal to 3.

The full-free quantity traversal combination is used for calculating the quantity of the flow under the condition of the full-quantity equipment parameter combination. In fact, the term "redundant computation" is too much, and the applicant has found that combining some fields together in advance can be used as a field, and then the traffic quantity under the condition of 1-3 field combinations of device parameters can be calculated in the following process to effectively meet the requirement. And the calculated amount is composed of

Is reduced to

In one embodiment, aggregating the fields to be aggregated to obtain a plurality of second fields to be combined includes:

The predetermined field correlation standard can be stored through a field correlation table, that is, the correlation between fields with correlation is stored in the field correlation table, and further, fields with correlation in fields to be aggregated can be searched for aggregation, so that the discrimination of coarse-grained fields (that is, the number of field data corresponding to the fields is small) can be effectively enhanced through the second field to be combined (that is, the number of accounts corresponding to the second field to be combined is obviously reduced compared with the number of accounts corresponding to a single field to be aggregated).

The field number of the second field to be combined, which is obtained by controlling the field to be combined to be randomly aggregated, is made to be less than or equal to a specific number, so that the effect of early aggregation can be ensured, and the total field number is less than or equal to the specific number in subsequent traversal combination.

In step S230, a first field data group corresponding to the device parameter variable field group is respectively obtained from the first account data, and a second field data group corresponding to the device parameter variable field group is respectively obtained from the second account data.

In this example embodiment, the first account data corresponds to a set of accounts to be identified, the first field data group corresponding to each device parameter variable field group is obtained from the first account data, and the first field data group corresponding to each device parameter variable field group of the account to be identified may be obtained.

The second account data corresponds to a white list account set, a second field data group corresponding to each device parameter variable field group is respectively obtained from the second account data, and the second field data group corresponding to each white list account is obtained.

In step S240, a number ratio of the account to be identified corresponding to each first field data group in the account set to be identified is obtained, and a number ratio of the white list account corresponding to each second field data group in the white list account set is obtained.

In the embodiment of the present example, the number proportion of the account to be recognized corresponding to each first field data group in the account set to be recognized is obtained, that is, the number proportion of the account to be recognized corresponding to each first field data group in all the accounts in the account set to be recognized is obtained; similarly, the number proportion of the white list accounts corresponding to each second field data group in the white list account set is obtained, that is, the number proportion of the white list accounts corresponding to each second field data group in all the accounts in the white list account set is obtained.

Thus, the following table shows the comparison of the number of the account numbers to be identified and the number of the white list account numbers in one example.

In step S250, comparing the number of accounts to be identified corresponding to each device parameter variable field group with the number of accounts in the white list, determining the abnormal device parameter variable field group, and determining the account to be identified corresponding to the abnormal device parameter variable field group as a false registered account.

In the embodiment of the present example, the number ratio of the to-be-identified accounts and the number ratio of the white list accounts corresponding to each device parameter variable field group are compared, that is, the number ratio of the to-be-identified accounts corresponding to the first field data group corresponding to the same device parameter variable field group and the number ratio of the white list accounts corresponding to the second field data group are obtained, and then the two are compared. Thus, when the number ratio of the account numbers to be identified is higher than the number ratio of the account numbers in the white list (the difference between the number ratio of the account numbers to be identified and the number ratio of the account numbers in the white list is greater than or equal to the predetermined ratio difference, or the ratio between the number ratio of the account numbers to be identified and the number ratio of the account numbers in the white list is greater than or equal to the predetermined ratio difference, or the first middle comparison condition (the number ratio of the account numbers to be identified is "very large" and the number ratio of the account numbers in the white list is "very small") is satisfied as shown in the above table, it is indicated that an abnormality exists in the first field data group corresponding to the same device parameter variable field group, which is different from a normal case, and the account numbers to be identified corresponding to the first field data group corresponding to the device parameter variable field group can be determined as a false registration account number.

According to the method and the system, the collected equipment parameters can be subjected to total statistics, usable data information data of the wind control can be fully utilized, and the coverage rate of identification is effectively improved; the method has stronger robustness to the variation of the black products and is not easy to be bypassed by the black products; the identification accuracy of false accounts is effectively improved.

As shown in fig. 4, the false registered account number identification apparatus 400 may include a first obtaining module 410, a combining module 420, a second obtaining module 430, a third obtaining module 440, and a determining module 450.

The first obtaining module 410 may be used as a first obtaining module, configured to obtain first account data corresponding to an account set to be identified, and obtain second account data corresponding to a white list account set, where the first account data and the second account data both include field data of a device parameter variable field;

the combination module 420 is configured to perform traversal combination on the device parameter variable fields to obtain multiple device parameter variable field groups;

the second obtaining module 430 is configured to obtain first field data groups corresponding to the device parameter variable field groups from the first account data, and obtain second field data groups corresponding to the device parameter variable field groups from the second account data;

the third obtaining module 440 is configured to obtain a number ratio of the to-be-identified account corresponding to the first field data group in the to-be-identified account set, and obtain a number ratio of the white list account corresponding to the second field data group in the white list account set;

the determining module 450 is configured to compare the number fraction of the account to be identified corresponding to each device parameter variable field group with the number fraction of the white list account, determine the abnormal device parameter variable field group, and determine the account to be identified corresponding to the abnormal device parameter variable field group as a false registration account.

It should be noted that although in the above detailed description several modules or units of the device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the application. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.

FIG. 5 schematically shows a block diagram of an electronic device according to an embodiment of the application.

It should be noted that 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 the application of the embodiments.

As shown in fig. 5, the electronic apparatus 500 includes a Central Processing Unit (CPU)501 that can perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)502 or a program loaded from a storage section 508 into a Random Access Memory (RAM) 503. In the RAM503, various programs and data necessary for system operation are also stored. The CPU 501, ROM 502, and RAM503 are connected to each other via a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.

The following components are connected to the I/O interface 505: an input portion 506 including a keyboard, a mouse, and the like; an output portion 507 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage portion 508 including a hard disk and the like; and a communication section 509 including a network interface card such as a LAN (local area network) card, a modem, or the like. The communication section 509 performs communication processing via a network such as the internet. The driver 510 is also connected to the I/O interface 505 as necessary. A removable medium 511 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 510 as necessary, so that a computer program read out therefrom is mounted into the storage section 508 as necessary.

In particular, according to embodiments of the present application, the processes described below with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 509, and/or installed from the removable medium 511. The computer program executes various functions defined in the system of the present application when executed by a Central Processing Unit (CPU) 501.

It should be noted that the computer readable medium shown in the present application may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer 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 of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, 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. In the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In this application, however, a computer readable signal medium may include a propagated data signal with computer 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 computer readable signal medium may also be any computer readable medium that is not a computer 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 computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF (radio frequency), etc., or any suitable combination of the foregoing.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units described in the embodiments of the present application may be implemented by software, or may be implemented by hardware, and the described units may also be disposed in a processor. Wherein the names of the elements do not in some way constitute a limitation on the elements themselves.

As another aspect, the present application also provides a computer-readable medium, which may be contained in the electronic device described in the above embodiments; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by an electronic device, cause the electronic device to implement the method described in the above embodiments.

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 application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can 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 can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiments of the present application.

Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains.

It will be understood that the present application is not limited to the embodiments that have been described above and shown in the drawings, but that various modifications and changes can be made without departing from the scope thereof.

Claims

1. A false registration account identification method is characterized by comprising the following steps:

acquiring first account data corresponding to an account set to be identified and second account data corresponding to a white list account set, wherein the first account data and the second account data both comprise field data of equipment parameter variable fields;

respectively acquiring a first field data group corresponding to each equipment parameter variable field group from the first account data, and respectively acquiring a second field data group corresponding to each equipment parameter variable field group from the second account data;

acquiring the number proportion of the accounts to be identified corresponding to each first field data group in the account set to be identified, and acquiring the number proportion of the white list accounts corresponding to each second field data group in the white list account set;

2. The method of claim 1, wherein the step of performing traversal combination on the device parameter variable fields to obtain a plurality of device parameter variable field sets comprises:

3. The method of claim 1, wherein the step of performing traversal combination on the device parameter variable fields to obtain a plurality of device parameter variable field sets comprises:

4. The method of claim 3, wherein the step of performing traversal combination on all the first fields to be combined and the second fields to be combined to obtain a plurality of field sets of device parameter variables comprises:

5. The method according to claim 3, wherein the aggregating the fields to be aggregated to obtain a plurality of second fields to be combined includes:

6. The method according to claim 3, wherein the aggregating the fields to be aggregated to obtain a plurality of second fields to be combined includes:

7. The method according to claim 1, wherein the acquiring first account data corresponding to the account set to be identified includes:

8. The method of claim 7, wherein the determining the abnormal target account number based on the number of account numbers comprises:

9. A false registration account identification apparatus, comprising:

10. An electronic device, comprising: a memory storing computer readable instructions; a processor reading computer readable instructions stored by the memory to perform the method of any of claims 1-8.