CN114049128A - High-speed passing blacklist setting method, device, server and medium - Google Patents

High-speed passing blacklist setting method, device, server and medium Download PDF

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CN114049128A
CN114049128A CN202111348514.2A CN202111348514A CN114049128A CN 114049128 A CN114049128 A CN 114049128A CN 202111348514 A CN202111348514 A CN 202111348514A CN 114049128 A CN114049128 A CN 114049128A
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user
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董亚东
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Bank of China Ltd
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    • G06Q20/102—Bill distribution or payments
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q20/00—Payment architectures, schemes or protocols
    • G06Q20/08—Payment architectures
    • G06Q20/14—Payment architectures specially adapted for billing systems

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Abstract

The application discloses a high-speed passing blacklist setting method, a high-speed passing blacklist setting device, a high-speed passing blacklist setting server and a high-speed passing blacklist setting medium, which can be applied to the technical field of artificial intelligence or the financial field. Acquiring first information of a user to be detected in a preset time period; acquiring second information of the user to be tested, wherein the second information represents whether the user to be tested complements the arrearage after the distance from the target duration of the preset time period; taking the first information as an input vector u (t), taking the second information as output information y (t), and inputting the output information y (t) into a pre-constructed state variable model to obtain a state variable x (t) output by the state variable model; calculating a first distance between the state variable x (t) corresponding to the user to be detected and the state variable x (t) of the user belonging to the blacklist; and if the first distance is greater than or equal to a first threshold value, adding the user to be tested to a blacklist. Thereby realizing automatic setting of the blacklist.

Description

High-speed passing blacklist setting method, device, server and medium
Technical Field
The application relates to the technical field of artificial intelligence, in particular to a high-speed passing blacklist setting method, a high-speed passing blacklist setting device, a server and a medium.
Background
ETC (Electronic Toll Collection) is a system for automatic Toll Collection on highways. The accounting and charging mode has the advantages of first passing and then deducting the toll, the accounting and charging mode means that the ETC accounting card is bound with the bank card of the user, and the user uses the ETC accounting card to pass through the toll station and then deducts the toll from the bound bank card according to the pass record. But the bank card bound by the user may not have sufficient funds to pay the toll and a arrears situation may occur. Financial institutions such as banks may set up blacklists to restrict such users from continuing to pass.
At present, the black list of the bank is time-consuming and labor-consuming to set manually.
Disclosure of Invention
In view of the above, the present application provides a method, an apparatus, a server and a medium for setting a high-speed passing blacklist.
In order to achieve the above purpose, the present application provides the following technical solutions:
according to a first aspect of the embodiments of the present disclosure, a method for setting a high-speed passing blacklist is provided, including:
acquiring first information of a user to be tested in a preset time period, wherein the first information comprises at least one of an Electronic Toll Collection (ETC) debit card number, the balance of a target account of a bank card bound with the ETC debit card, the number of vehicles associated with the target account, the daily average running water of the target account, the daily average balance of the target account, the accumulated debt amount of the ETC debit card, the accumulated debt number of the ETC debit card, the average daily pass amount of the ETC debit card, the average daily pass number of the ETC debit card, the province of the user to be tested and asset information corresponding to the target account;
acquiring second information of the user to be tested, wherein the second information represents whether the user to be tested complements the arrearage after the distance from the target duration of the preset time period;
taking the first information as an input vector u (t), taking the second information as output information y (t), and inputting the output information y (t) into a pre-constructed state variable model to obtain a state variable x (t) output by the state variable model;
the state variable model takes first information of a sample user as an input vector u (t), takes second information of the sample user as output information y (t), and trains an echo state network x (t) tanh (W)inu(t)+Wresx(t-1))，y(t)＝WoutX (t), wherein x (t) is an internal state variable of the echo state network, Win、Wres、WoutIs a preset value;
calculating a first distance between the state variable x (t) corresponding to the user to be detected and the state variable x (t) of the user belonging to the blacklist;
and if the first distance is greater than or equal to a first threshold value, adding the user to be tested to a blacklist.
According to a second aspect of the embodiments of the present disclosure, there is provided a high-speed passing blacklist setting apparatus, including:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring first information of a user to be tested in a preset time period, and the first information comprises at least one of an Electronic Toll Collection (ETC) debit card number, a balance of a target account of a bank card bound with the ETC debit card, the number of vehicles associated with the target account, daily average running water of the target account, daily average balance of the target account, accumulated debt amount of the ETC debit card, accumulated debt number of the ETC debit card, average daily pass amount of the ETC debit card, average daily pass number of the ETC debit card, province of the user to be tested and asset information corresponding to the target account;
the second obtaining module is used for obtaining second information of the user to be tested, and the second information represents whether the user to be tested complements the arrearage after the distance from the target duration of the preset time period;
the third obtaining module is used for taking the first information as an input vector u (t), taking the second information as output information y (t), and inputting the output information y (t) into a pre-constructed state variable model to obtain a state variable x (t) output by the state variable model;
the state variable model takes first information of a sample user as an input vector u (t), takes second information of the sample user as output information y (t), and trains an echo state network x (t) tanh (W)inu(t)+Wresx(t-1))，y(t)＝WoutX (t), wherein x (t) is an internal state variable of the echo state network, Win、Wres、WoutIs a preset value;
a first calculating module, configured to calculate a first distance between the state variable x (t) corresponding to the user to be tested and a state variable x (t) of a user belonging to a blacklist;
and the adding module is used for adding the user to be tested to a blacklist if the first distance is greater than or equal to a first threshold.
According to a third aspect of the embodiments of the present disclosure, there is provided a server, including:
a processor;
a memory for storing the processor-executable instructions;
wherein the processor is configured to execute the instructions to implement the high speed traffic blacklist setting method according to the first aspect.
According to a fourth aspect of embodiments of the present disclosure, there is provided a computer-readable storage medium, wherein instructions of the storage medium, when executed by a processor of a server, enable the electronic device to perform the high-speed passage blacklist setting method according to the first aspect.
According to a fifth aspect of the embodiments of the present disclosure, there is provided a computer program product directly loadable into an internal memory of a computer, wherein the memory is included in the server shown in the third aspect and contains software codes, and the computer program can be loaded into and executed by the computer to implement the high-speed passing blacklist setting method according to the first aspect.
According to the technical scheme, in the high-speed passing blacklist setting method provided by the embodiment of the application, first information of a user to be tested in a preset time period is acquired; acquiring second information of the user to be tested, wherein the second information represents whether the user to be tested complements the arrearage after the distance from the target duration of the preset time period; taking the first information as an input vector u (t), taking the second information as output information y (t), and inputting the output information y (t) into a pre-constructed state variable model to obtain a state variable x (t) output by the state variable model; calculating a first distance between the state variable x (t) corresponding to the user to be detected and the state variable x (t) of the user belonging to the blacklist; and if the first distance is greater than or equal to a first threshold value, adding the user to be tested to a blacklist. Thereby realizing automatic setting of the blacklist.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
FIG. 1 is a diagram illustrating a hardware architecture according to an embodiment of the present application;
fig. 2 is a flowchart of a method for setting a high-speed passing blacklist according to an embodiment of the present application;
fig. 3 is a structural diagram of a high-speed passing blacklist setting apparatus according to an embodiment of the present application;
FIG. 4 is a block diagram illustrating an apparatus for an electronic device in accordance with an example embodiment.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the 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 application.
The embodiment of the application provides a high-speed passing blacklist setting method, a high-speed passing blacklist setting device, a server and a medium. Before introducing the technical solutions provided by the embodiments of the present application, a hardware architecture related to the embodiments of the present application is described.
Fig. 1 is a schematic diagram of a hardware architecture according to an embodiment of the present application. The hardware architecture comprises: an electronic device 11 and a server 12.
The electronic device 11 may be any electronic product capable of interacting with a user through one or more ways, such as a keyboard, a touch PAD, a touch screen, a remote controller, a voice interaction device, or a handwriting device, for example, a mobile phone, a notebook computer, a tablet computer, a palm computer, a personal computer, a wearable device, a smart television, a PAD, and the like.
Illustratively, the electronic device 11 may also be a toll collection device at the ETC passage. The electronic device 11 may also be, for example, an ATM machine of a banking outlet.
Illustratively, the electronic device 11 may have a client running therein, and the client may be a mobile banking application.
The server 12 may be, for example, one server, a server cluster composed of a plurality of servers, or a cloud computing server center. The server 12 may include a processor, memory, and a network interface, among others.
It should be noted that fig. 1 is only an example, and the type of the electronic device 11 may be various and is not limited to the smartphone in fig. 1.
For example, the server 12 may collect the first information and the second information of the user through the electronic device 11.
Illustratively, the server 12 may obtain the first information and the second information of the user through a server of a bank. It may thus be determined whether the user needs to be added to the blacklist based on the first information and the second information.
It will be understood by those skilled in the art that the foregoing electronic devices and servers are merely exemplary and that other existing or future electronic devices or servers may be suitable for use with the present disclosure and are intended to be included within the scope of the present disclosure and are hereby incorporated by reference.
The following describes a high-speed passing blacklist setting method provided by the embodiment of the present application with reference to the above hardware architecture.
As shown in fig. 2, a flowchart of a method for setting a high-speed passing blacklist provided in an embodiment of the present application may be applied to the electronic device 11 shown in fig. 1, and the method involves the following steps S21 to S25 in implementation.
Step S21: the method comprises the steps of obtaining first information of a user to be detected in a preset time period.
The first information comprises an Electronic Toll Collection (ETC) debit card number, the grade of the user to be tested, the balance of a target account of a bank card bound by the ETC debit card, the number of vehicles associated with the target account, the daily average running water of the target account, the daily average balance of the target account, the accumulated debt amount of the ETC debit card, the accumulated debt number of the ETC debit card, the average daily traffic amount of the ETC debit card, the average daily traffic number of the ETC debit card, the province of the user to be tested and at least one of asset information corresponding to the target account.
The first information belongs to a preset time period, which may be [ current time-target duration, current time + preset duration ] as an example. For example, the target duration and the preset duration may be based on time settings.
For example, the length of the preset time period may be N days, where N is any value greater than or equal to 1.
For example, the ETC card number is a unique identifier of an ETC device installed on a vehicle.
Exemplary asset information corresponding to a target account includes, but is not limited to: at least one of property information, loan information, investment information, and a balance sum of the bank card.
Illustratively, the target account is an account of a bank card bound with the ETC.
Illustratively, the target account is identity information of the user to be tested. The balance of the target account includes the balance of all the bank cards of the user name to be tested in a preset time period, the number of vehicles associated with the target account is the number of all the vehicles of the user name to be tested, the daily average running water of the target account is the daily average running water of all the bank cards of the user to be tested in the preset time period, and the daily average balance of the target account is the daily average balance of all the bank cards of the user to be tested in the preset time period.
Illustratively, the first information further includes a gender of the user to be tested. Illustratively, the first information further includes: status of the target account.
Illustratively, the rating of the user to be tested is based on the credit score of the user to be tested.
Illustratively, the rank of the user to be tested is preset by the bank.
For example, the first information of the user to be tested is as follows:
ETC accounting card number: 5575757572727, respectively; balance of target account: 255.33, respectively; grade of the user to be tested: 5; number of vehicles associated with the bid account: 2; average running water per day for the target account: 25.36; average daily balance of target account: 1000.0; number of vehicles associated with target account: 2; target account status: 0022; ETC accounting card accumulated arrearage amount: 12.6; the ETC accounting card accumulates the defaulting number: 1; average daily traffic stroke number of the ETC debit card: 3; average daily passage amount of ETC accounting card: 22.3; the province of the user to be tested is as follows: 00003; sum of balance of bank card: 23100.96, respectively; sex: 1.
illustratively, gender women are characterized by 1 and gender men are characterized by 0. Provinces are identified by different strings.
Step S22: and acquiring second information of the user to be tested, wherein the second information represents whether the user to be tested complements the arrearage after the distance to the target duration of the preset time period.
For example, the target duration may be one week.
If the user to be tested does not have the ETC arrearage after the target time length of the preset time period is reached, or the ETC arrearage is complemented, the second information can be represented by the first character; if the ETC arrearage is not complemented by the user to be tested after the target time length of the preset time period, the second information can be represented by a second character.
The first character is different from the second character, for example, the first character is 1 and the second character is 0.
Step S23: and taking the first information as an input vector u (t), taking the second information as output information y (t), and inputting the output information y (t) into a pre-constructed state variable model to obtain a state variable x (t) output by the state variable model.
The state variable model takes first information of a sample user as an input vector u (t), takes second information of the sample user as output information y (t), and trains an echo state network x (t) tanh (W)inu(t)+Wresx(t-1))，y(t)＝WoutX (t), wherein x (t) is an internal state variable of the echo state network, Win、Wres、WoutIs a preset value.
In an alternative implementation, in the case of x (t) ═ tanh (W)inu(t)+Wresx (t-1)) is initialized, x (0) is set to 0, and W is set to WinInitialization of (2): winThe element in (1) can be in [ -scale, scale]With even distribution therebetween, an exemplary scale may be set to 1 first.
Exemplary, WresInitialization of (2): first step random generation of matrix W1The spectrum radius ρ obtained in the second step is α ═ ρ W in consideration of the convergence of the echo state transfer functionresAnd α represents WresSpectral radius, where alpha > 1, is to be ensured, which is, for example, taken to be 2, the spectral radius ρ of the internal cells of the reservoir is the reservoir state variable WresThe eigenvalue with the largest absolute value.
In the embodiment of the application, the distance between the user and the state variable x (t) of the user is skillfully calculated. Instead of calculating the distance between the user and the user by using the output information of the user, the calculated distance is more accurate for the following reasons:
since x (t + t1) ═ tanh (W)inu(t+t1)+Wresx (t + t1-1)) as time t1 increases, the effect from the x (t) state gradually decreases or even disappears. This is advantageous because the reservoir status x (0) of the echo status network is initialized randomly at time t-0, and the initialization information needs to be removed slowly to make the entire status network reach a stable status. However, if the distance between the two is calculated using the output information, the influence of the initialization information cannot be removed.
Next, step S23 will be described by way of example. If the first information of the user to be tested is as follows:
ETC accounting card number: 5575757572727, respectively; balance of target account: 255.33, respectively; grade of the user to be tested: 5; number of vehicles associated with the bid account: 2; average running water per day for the target account: 25.36; average daily balance of target account: 1000.0; number of bank cards under account name: 2; target account status: 0022; ETC accounting card accumulated arrearage amount: 12.6; the ETC accounting card accumulates the defaulting number: 1; average daily traffic stroke number of the ETC debit card: 3; average daily passage amount of ETC accounting card: 22.3; the province of the user to be tested is as follows: 00003; sum of balance of bank card: 23100.96, respectively; sex: 1. then the input vector u (t) is {5575757572727, 255.33, 5, 2, 25.36, 1000.0, 2, 0022, 12.6, 1, 3, 22.3, 00003, 23100.96, 1 }.
Step S24: and calculating a first distance between the state variable x (t) corresponding to the user to be detected and the state variable x (t) of the user belonging to the blacklist.
Illustratively, the first distance may be a euclidean distance.
Illustratively, the blacklist may include one or more users. The distance between the state variable x (t) of the user to be detected and the state variable x (t) of each user in the blacklist can be calculated; and then taking the average value of the distances between each user in the blacklist and the user to be detected as a first distance.
In an alternative implementation, a trigger condition may also be included before step S24.
Illustratively, the trigger conditions are: if it is detected that the deduction fails when the toll deduction operation is performed on the target account, performing the step S24; or, if the user to be tested belongs to the blacklist and the user to be tested has already cleared the debt, executing the step S24.
I.e. in case the user to be tested is likely to be referred to as a blacklisted user, step S24 is performed.
Step S25: and if the first distance is greater than or equal to a first threshold value, adding the user to be tested to a blacklist.
For example, the first threshold may be preset.
For example, the first threshold may be obtained as follows: calculating a second distance between the state variable x (t) corresponding to the user to be detected and the state variable x (t) of the user belonging to the white list; determining the second distance as the first threshold.
Illustratively, the second distance may be a euclidean distance. The calculation manner of the second distance may refer to the first distance, which is not described herein.
In the method for setting the high-speed passing blacklist, first information of a user to be tested in a preset time period is acquired; acquiring second information of the user to be tested, wherein the second information represents whether the user to be tested complements the arrearage after the distance from the target duration of the preset time period; taking the first information as an input vector u (t), taking the second information as output information y (t), and inputting the output information y (t) into a pre-constructed state variable model to obtain a state variable x (t) output by the state variable model; calculating a first distance between the state variable x (t) corresponding to the user to be detected and the state variable x (t) of the user belonging to the blacklist; and if the first distance is greater than or equal to a first threshold value, adding the user to be tested to a blacklist. Thereby implementing the setting of the blacklist.
The method is described in detail in the embodiments disclosed in the present application, and the method of the present application can be implemented by various types of apparatuses, so that an apparatus is also disclosed in the present application, and the following detailed description is given of specific embodiments.
As shown in fig. 3, a block diagram of a device for setting a high-speed passing blacklist provided in an embodiment of the present application includes: a first obtaining module 31, a second obtaining module 32, a third obtaining module 33, a first calculating module 34, and an adding module 35, wherein:
the first obtaining module 31 is configured to obtain first information of a user to be tested in a preset time period, where the first information includes at least one of an Electronic Toll Collection (ETC) debit card number, a balance of a target account of a bank card bound to the ETC debit card, a number of vehicles associated with the target account, a daily average running water of the target account, a daily average balance of the target account, an accumulated debt amount of the ETC debit card, an accumulated debt number of the ETC debit card, an average daily traffic amount of the ETC debit card, an average daily traffic number of the ETC debit card, a province where the user to be tested is located, and asset information corresponding to the target account;
a second obtaining module 32, configured to obtain second information of the user to be tested, where the second information represents whether the user to be tested makes up the arrearage after the distance from the preset time period to the target duration;
a third obtaining module 33, configured to use the first information as an input vector u (t), and use the second information as output information y (t), and input the output information y (t) to a pre-constructed state variable model to obtain a state variable x (t) output by the state variable model;
the state variable model takes first information of a sample user as an input vector u (t), takes second information of the sample user as output information y (t), and trains an echo state network x (t) tanh (W)inu(t)+Wresx(t-1))，y(t)＝WoutX (t), wherein x (t) is an internal state variable of the echo state network, Win、Wres、WoutIs a preset value;
a first calculating module 34, configured to calculate a first distance between the state variable x (t) corresponding to the user to be tested and a state variable x (t) of a user belonging to a blacklist;
an adding module 35, configured to add the user to be tested to a blacklist if the first distance is greater than or equal to a first threshold.
In an optional implementation manner, the method further includes:
the first triggering module is used for triggering the first calculating module if the deduction failure is detected when the toll deduction operation is executed aiming at the target account.
In an optional implementation manner, the method further includes:
and the second triggering module is used for triggering the first calculating module if the user to be detected belongs to the blacklist and the user to be detected has paid back and debt.
In an optional implementation manner, the method further includes:
a second calculating module, configured to calculate a second distance between the state variable x (t) corresponding to the user to be tested and a state variable x (t) of a user belonging to a white list;
a determining module, configured to determine that the second distance is the first threshold.
With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
Fig. 4 is a block diagram illustrating an apparatus for a server in accordance with an example embodiment.
Servers include, but are not limited to: a processor 41, a memory 42, a network interface 43, an I/O controller 44, and a communication bus 45.
It should be noted that the structure of the server shown in fig. 4 is not limited to the server, and the server may include more or less components than those shown in fig. 4, or some components may be combined, or a different arrangement of components may be used, as will be understood by those skilled in the art.
The following describes each component of the server in detail with reference to fig. 4:
the processor 41 is a control center of the server, connects various parts of the entire server using various interfaces and lines, and performs various functions of the server and processes data by running or executing software programs and/or modules stored in the memory 42 and calling data stored in the memory 42, thereby performing overall monitoring of the server. Processor 41 may include one or more processing units; illustratively, the processor 41 may integrate an application processor, which primarily handles operating systems, user interfaces, applications, etc., and a modem processor, which primarily handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 41.
Processor 41 may be a Central Processing Unit (CPU), or an application Specific Integrated circuit (asic), or one or more Integrated circuits configured to implement embodiments of the present invention, etc.;
the Memory 42 may include Memory, such as a Random-Access Memory (RAM) 421 and a Read-Only Memory (ROM) 422, and may also include a mass storage device 423, such as at least 1 disk storage. Of course, the server may also include hardware needed for other services.
The memory 42 is configured to store the executable instructions of the processor 41. The processor 41 has the following functions:
acquiring first information of a user to be tested in a preset time period, wherein the first information comprises at least one of an Electronic Toll Collection (ETC) debit card number, the grade of the user to be tested, the balance of a target account of a bank card bound with the ETC debit card, the number of vehicles related to the target account, the daily average running water of the target account, the daily average balance of the target account, the accumulated owing amount of the ETC debit card, the accumulated owing number of the ETC debit card, the average daily passing amount of the ETC debit card, the average daily passing number of the ETC debit card, the province where the user to be tested is located and asset information corresponding to the target account;
acquiring second information of the user to be tested, wherein the second information represents whether the user to be tested complements the arrearage after the distance from the target duration of the preset time period;
taking the first information as an input vector u (t), taking the second information as output information y (t), and inputting the output information y (t) into a pre-constructed state variable model to obtain a state variable x (t) output by the state variable model;
the state variable model takes first information of a sample user as an input vector u (t), takes second information of the sample user as output information y (t), and trains an echo state network x (t) tanh (W)inu(t)+Wresx(t-1))，y(t)＝WoutX (t), wherein x (t) is an internal state variable of the echo state network, Win、Wres、WoutTo prepareSetting a numerical value;
calculating a first distance between the state variable x (t) corresponding to the user to be detected and the state variable x (t) of the user belonging to the blacklist;
and if the first distance is greater than or equal to a first threshold value, adding the user to be tested to a blacklist.
A wired or wireless network interface 43 is configured to connect the server to a network.
The processor 41, the memory 42, the network interface 43, and the I/O controller 44 may be connected to each other by a communication bus 45, which may be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc.
In an exemplary embodiment, the server may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic components for performing the high-speed passing blacklist setting method between the above knowledge points.
In an exemplary embodiment, the disclosed embodiments provide a storage medium comprising instructions, such as the memory 42 comprising instructions, executable by the processor 41 of the server to perform the above-described method. Alternatively, the storage medium may be a non-transitory computer readable storage medium, which may be, for example, a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
In an exemplary embodiment, a computer readable storage medium is provided, which can be directly loaded into the internal memory of a computer, such as the memory 42, and contains software codes, and the computer program can be loaded into the computer and executed to implement the steps of any embodiment of the high-speed passing blacklist setting method.
In an exemplary embodiment, a computer program product is also provided, which is directly loadable into an internal memory of a computer, such as a memory included in the server, and contains software codes, and which, when loaded and executed by the computer, is capable of implementing the steps of any of the embodiments of the high-speed passing blacklist setting method described above.
It should be noted that the high-speed passing blacklist setting method, device, server and medium provided by the invention can be used in the technical field of artificial intelligence or the financial field. The above is merely an example, and the application fields of the high-speed passing blacklist setting method, device, server and medium provided by the present invention are not limited.
Note that the features described in the embodiments in the present specification may be replaced with or combined with each other. For the device or system type embodiment, since it is basically similar to the method embodiment, the description is simple, and for the relevant points, refer to the partial description of the method embodiment.
It is further noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (10)

1. A high-speed passing blacklist setting method is characterized by comprising the following steps:
acquiring first information of a user to be tested in a preset time period, wherein the first information comprises at least one of an Electronic Toll Collection (ETC) debit card number, the grade of the user to be tested, the balance of a target account of a bank card bound with the ETC debit card, the number of vehicles related to the target account, the daily average running water of the target account, the daily average balance of the target account, the accumulated owing amount of the ETC debit card, the accumulated owing number of the ETC debit card, the average daily passing amount of the ETC debit card, the average daily passing number of the ETC debit card, the province where the user to be tested is located and asset information corresponding to the target account;
acquiring second information of the user to be tested, wherein the second information represents whether the user to be tested complements the arrearage after the distance from the target duration of the preset time period;
taking the first information as an input vector u (t), taking the second information as output information y (t), and inputting the output information y (t) into a pre-constructed state variable model to obtain a state variable x (t) output by the state variable model;
wherein the state variable model is a model using samplesUsing the first information of the user as an input vector u (t), using the second information of the sample user as output information y (t), and training an echo state network x (t) ═ tanh (W)inu(t)+Wresx(t-1))，y(t)＝WoutX (t), wherein x (t) is an internal state variable of the echo state network, Win、Wres、WoutIs a preset value;
calculating a first distance between the state variable x (t) corresponding to the user to be detected and the state variable x (t) of the user belonging to the blacklist;
and if the first distance is greater than or equal to a first threshold value, adding the user to be tested to a blacklist.
2. The method for setting the high-speed passing blacklist according to claim 1, further comprising:
and if the deduction failure in the process of deducting the toll from the target account is detected, executing a first distance step of calculating the state variable x (t) corresponding to the user to be tested and the state variable x (t) of the user belonging to the blacklist.
3. The method for setting the high-speed passing blacklist according to claim 1, further comprising:
and if the user to be detected belongs to the blacklist and the user to be detected has lost money, executing a first distance step of calculating the state variable x (t) corresponding to the user to be detected and the state variable x (t) of the user belonging to the blacklist.
4. The method for setting the high-speed passing blacklist according to any one of claims 1 to 3, wherein the method for obtaining the first threshold value comprises:
calculating a second distance between the state variable x (t) corresponding to the user to be detected and the state variable x (t) of the user belonging to the white list;
determining the second distance as the first threshold.
5. A high-speed passage blacklist setting apparatus, comprising:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring first information of a user to be tested in a preset time period, and the first information comprises at least one of an Electronic Toll Collection (ETC) debit card number, a balance of a target account of a bank card bound with the ETC debit card, the number of vehicles associated with the target account, daily average running water of the target account, daily average balance of the target account, accumulated debt amount of the ETC debit card, accumulated debt number of the ETC debit card, average daily pass amount of the ETC debit card, average daily pass number of the ETC debit card, province of the user to be tested and asset information corresponding to the target account;
the second obtaining module is used for obtaining second information of the user to be tested, and the second information represents whether the user to be tested complements the arrearage after the distance from the target duration of the preset time period;
the third obtaining module is used for taking the first information as an input vector u (t), taking the second information as output information y (t), and inputting the output information y (t) into a pre-constructed state variable model to obtain a state variable x (t) output by the state variable model;
the state variable model takes first information of a sample user as an input vector u (t), takes second information of the sample user as output information y (t), and trains an echo state network x (t) tanh (W)inu(t)+Wresx(t-1))，y(t)＝WoutX (t), wherein x (t) is an internal state variable of the echo state network, Win、Wres、WoutIs a preset value;
a first calculating module, configured to calculate a first distance between the state variable x (t) corresponding to the user to be tested and a state variable x (t) of a user belonging to a blacklist;
and the adding module is used for adding the user to be tested to a blacklist if the first distance is greater than or equal to a first threshold.
6. The apparatus of claim 5, further comprising:
the first triggering module is used for triggering the first calculating module if the deduction failure is detected when the toll deduction operation is executed aiming at the target account.
7. The apparatus of claim 5, further comprising:
and the second triggering module is used for triggering the first calculating module if the user to be detected belongs to the blacklist and the user to be detected has paid back and debt.
8. The apparatus for setting a blacklist for high speed traffic according to any one of claims 5 to 7, further comprising:
a second calculating module, configured to calculate a second distance between the state variable x (t) corresponding to the user to be tested and a state variable x (t) of a user belonging to a white list;
a determining module, configured to determine that the second distance is the first threshold.
9. A server, comprising:
a processor;
a memory for storing the processor-executable instructions;
wherein the processor is configured to execute the instructions to implement the high speed transit blacklist setting method of any one of claims 1 to 4.
10. A computer-readable storage medium, in which instructions, when executed by a processor of an electronic device, enable the server to perform the high-speed transit blacklist setting method of any one of claims 1 to 4.
CN202111348514.2A 2021-11-15 2021-11-15 High-speed passing blacklist setting method, device, server and medium Pending CN114049128A (en)

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CN202111348514.2A CN114049128A (en) 2021-11-15 2021-11-15 High-speed passing blacklist setting method, device, server and medium

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202111348514.2A CN114049128A (en) 2021-11-15 2021-11-15 High-speed passing blacklist setting method, device, server and medium

Publications (1)

Publication Number Publication Date
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Country Status (1)

Country Link
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