Cross-bank transaction processing method and device, server and storage medium

Abstract

The embodiment of the invention provides an inter-bank transaction processing method and device, a server and a storage medium. Relates to the technical field of transaction processing. The method comprises the following steps: receiving first face recognition data sent by a preset inter-row terminal; if it is determined that the first face recognition data comprises face recognition failure data, determining a plurality of related terminals related to the cross-row terminal in a preset terminal set; wherein the face recognition failure data is recognition data of which the number of continuous face recognition failures is greater than a preset failure number threshold. According to the related terminal, determining a cash threshold value corresponding to the face recognition failure data, and sending the cash threshold value to an inter-bank terminal; wherein the cash threshold value is used for controlling the amount of the target cash transaction of the inter-bank terminal after the face recognition is successful. The method is used for achieving the effect of improving the safety of inter-bank transactions.

Description

Technical Field

[0001] The present application relates to the field of transaction processing technology, and in particular to an inter-bank transaction processing method, device, server and storage medium. Background Art

[0002] Currently, the most widespread form of transaction is cash circulation, so in order to reduce transaction risks, cash transactions need to be controlled.

[0003] In the prior art, users can deposit and withdraw money at a terminal to realize money circulation.

[0004] However, in the prior art, if a user operates at an inter-bank terminal, the security of inter-bank cash transactions is low due to the lack of risk information sharing between inter-bank systems. Summary of the invention

[0005] The embodiments of the present application provide an inter-bank transaction processing method, device, server and storage medium to achieve the effect of improving the security of inter-bank transactions.

[0006] In a first aspect, an embodiment of the present application provides an inter-bank transaction processing method, including:

[0007] Receiving first face recognition data sent by a preset inter-bank terminal;

[0008] If it is determined that the first face recognition data includes face recognition failure data, then in a preset terminal set, a plurality of related terminals related to the inter-bank terminal are determined; wherein the face recognition failure data is recognition data in which the number of consecutive face recognition failures is greater than a preset failure number threshold;

[0009] According to the relevant terminal, a cash threshold corresponding to the face recognition failure data is determined, and the cash threshold is sent to the inter-bank terminal; wherein the cash threshold is used to control the amount of the target cash transaction of the inter-bank terminal after the face recognition is successful.

[0010] In a possible implementation, if it is determined that the first face recognition data includes face recognition failure data, a plurality of related terminals related to the inter-bank terminal are determined in a preset terminal set, including:

[0011] If it is determined that the first face recognition data includes face recognition failure data, then, in a preset terminal set, respectively determining a physical distance between each terminal in the terminal set and the inter-bank terminal;

[0012] If it is determined that there is a target physical distance that is less than the preset physical distance threshold, the terminal corresponding to the target physical distance is used as the relevant terminal;

[0013] If there is no target physical distance less than the physical distance threshold, obtaining the first category of merchants of each terminal in the terminal set and the second category of merchants of the inter-bank terminal; wherein the physical distance between each terminal and the first category of merchants is less than a preset first threshold, and the physical distance between the inter-bank terminal and the second category of merchants is less than a preset second threshold;

[0014] According to the first category of merchants and the second category of merchants, a plurality of related terminals related to the inter-bank terminal are selected from the terminal set.

[0015] In a possible implementation, determining, according to the relevant terminal, a cash threshold corresponding to the face recognition failure data includes:

[0016] Acquire multiple second face recognition data and cash transaction data of the relevant terminal; wherein each second face recognition data includes multiple consecutive failed face recognitions and successful face recognitions after the failures;

[0017] Determine a similarity coefficient between each second face recognition data in the plurality of second face recognition data and a plurality of failed face recognitions in the face recognition failure data of the inter-bank terminal;

[0018] The second face recognition data corresponding to the similarity coefficient greater than the preset similarity threshold is used as the similar face recognition data, and the cash transaction data executed after the similar face recognition data is used as the potential cash transaction data;

[0019] A cash threshold corresponding to multiple failed face recognitions in the face recognition failure data is determined based on the potential cash transaction data.

[0020] In a possible implementation manner, determining a similarity coefficient between each second face recognition data in the plurality of second face recognition data and a plurality of failed face recognitions in the face recognition failure data of the inter-bank terminal includes:

[0021] For each failed face recognition included in the second face recognition data, determine a first difference between a first preset face recognition matching threshold corresponding to the failed face recognition and a first face recognition matching value, and use the first difference as a first face recognition difference value of the second face recognition data;

[0022] For each failed face recognition in the multiple failed face recognitions of the face recognition failure data, determine a second difference between a second preset face recognition matching threshold of the failed face recognition and a second face recognition matching value, and use the second difference as a second face recognition difference corresponding to the multiple failed face recognitions in the face recognition failure data;

[0023] Taking the first face recognition difference as a first type of difference set, and taking the set consisting of the second face recognition difference as a second type of difference set;

[0024] Based on the first type of difference set and the second type of difference set, a similarity coefficient between the second face recognition data and multiple failed face recognitions in the face recognition failure data is determined.

[0025] In a possible implementation, determining, based on the first type of difference set and the second type of difference set, a similarity coefficient between the second face recognition data and multiple failed face recognitions in the face recognition failure data includes:

[0026] Determine the number of first differences included in the first type of difference set and the number of second differences included in the second type of difference set; and determine the minimum value of the first number of differences and the second number of differences;

[0027] Constructing a difference space according to the first difference value included in the first type of difference value set, the second difference value included in the second type of difference value set, and the minimum value; wherein the number of dimensions of the difference space is equal to the minimum value, and each dimension corresponds to a difference value;

[0028] Determine a plurality of first-class difference subsets of the first-class difference set; wherein the number of elements contained in the first-class difference subset is equal to the number of dimensions of the difference space; for each first-class difference subset, sort the elements of the first-class difference subset in ascending order, and correspond the elements to the dimensions of the difference space one by one in sequence; determine a first spatial point of the first-class difference subset in the difference space; wherein the value of the first spatial point in each dimension is equal to the element of the first-class difference subset corresponding to the dimension;

[0029] Determine a plurality of second-class difference subsets of the second-class difference set; wherein the number of elements contained in the second-class difference subset is equal to the number of dimensions of the difference space; for each second-class difference subset, sort the elements of the second-class difference subset in ascending order, and correspond the elements to the dimensions of the difference space one by one in sequence; determine a second spatial point of the second-class difference subset in the difference space; wherein the value of the second spatial point in each dimension is equal to the element of the second-class difference subset corresponding to the dimension;

[0030] In the difference space, determining the spatial distance between each first spatial point of the first type of difference subset and each second spatial point of the second type of difference subset;

[0031] Based on the minimum spatial distance among the spatial distances, a similarity coefficient between the second face recognition data and the multiple failed face recognitions in the face recognition failure data is determined.

[0032] In a possible implementation, determining, based on the first type of difference set and the second type of difference set, a similarity coefficient between the second face recognition data and multiple failed face recognitions in the face recognition failure data includes:

[0033] determining a preset difference threshold;

[0034] The number of first face recognition difference values ​​less than the difference threshold in the first type of difference set is used as the first number of the first type of difference set; the number of face recognition difference values ​​greater than or equal to the difference threshold in the first type of difference set is used as the second number of the first type of difference set;

[0035] The number of face recognition difference values ​​less than the difference threshold in the second type of difference value set is used as the third number of the second type of difference value set; the number of face recognition difference values ​​greater than or equal to the difference threshold in the second type of difference value set is used as the fourth number of the second type of difference value set;

[0036] Based on the first number, the second number, the third number, and the fourth number, a similarity coefficient between the second face recognition data and multiple failed face recognitions in the face recognition failure data is determined.

[0037] In a possible implementation, determining, based on the potential cash transaction data, a cash threshold corresponding to multiple failed face recognitions in the face recognition failure data includes:

[0038] Get multiple preset potential cash thresholds;

[0039] For each potential cash threshold, determine target potential cash transaction data whose transaction amount is less than the potential cash threshold according to the transaction amount in the potential cash transaction data; and use the target potential cash transaction data as the potential cash transaction data of the potential cash threshold;

[0040] determining an abnormality coefficient of the potential cash threshold based on target potential cash transaction data of the potential cash threshold;

[0041] According to the abnormal coefficient of each potential cash threshold, the cash threshold corresponding to multiple failed face recognitions in the face recognition failure data is determined.

[0042] In a second aspect, an embodiment of the present application provides an inter-bank transaction processing device, including:

[0043] A receiving module, used for receiving first face recognition data sent by a preset inter-bank terminal;

[0044] A first determination module, configured to determine, if it is determined that the first face recognition data includes face recognition failure data, a plurality of related terminals related to the inter-bank terminal in a preset terminal set; wherein the face recognition failure data is recognition data in which the number of consecutive face recognition failures is greater than a preset failure number threshold;

[0045] A second determination module is used to determine the cash threshold corresponding to the face recognition failure data according to the relevant terminal;

[0046] A sending module is used to send the cash threshold to the inter-bank terminal; wherein the cash threshold is used to control the amount of the target cash transaction of the inter-bank terminal after the face recognition is successful.

[0047] In a possible implementation manner, the first determining module is specifically configured to:

[0048] If it is determined that the first face recognition data includes face recognition failure data, then, in a preset terminal set, respectively determining a physical distance between each terminal in the terminal set and the inter-bank terminal;

[0049] If it is determined that there is a target physical distance that is less than the preset physical distance threshold, the terminal corresponding to the target physical distance is used as the relevant terminal;

[0050] If there is no target physical distance less than the physical distance threshold, obtaining the first category of merchants of each terminal in the terminal set and the second category of merchants of the inter-bank terminal; wherein the physical distance between each terminal and the first category of merchants is less than a preset first threshold, and the physical distance between the inter-bank terminal and the second category of merchants is less than a preset second threshold;

[0051] According to the first category of merchants and the second category of merchants, a plurality of related terminals related to the inter-bank terminal are selected from the terminal set.

[0052] In a possible implementation manner, the second determining module includes:

[0053] An acquisition unit, used to acquire a plurality of second face recognition data and cash transaction data of the relevant terminal; wherein each second face recognition data includes a plurality of consecutive failed face recognitions and a successful face recognition after the failure;

[0054] A first determining unit is used to determine a similarity coefficient between each second face recognition data in the plurality of second face recognition data and a plurality of failed face recognitions in the face recognition failure data of the inter-bank terminal;

[0055] A second determining unit is used to use the second face recognition data corresponding to the similarity coefficient greater than a preset similarity threshold as the similar face recognition data, and use the cash transaction data executed after the similar face recognition data as the potential cash transaction data;

[0056] The third determination unit is used to determine a cash threshold corresponding to multiple failed face recognitions in the face recognition failure data based on the potential cash transaction data.

[0057] In a possible implementation manner, the first determining unit includes:

[0058] a first determining subunit, configured to determine, for each failed face recognition included in the second face recognition data, a first difference between a first preset face recognition matching threshold corresponding to the failed face recognition and a first face recognition matching value, and use the first difference as a first face recognition difference value of the second face recognition data;

[0059] A second determining subunit is configured to determine, for each failed face recognition in the multiple failed face recognitions of the face recognition failure data, a second difference between a second preset face recognition matching threshold of the failed face recognition and a second face recognition matching value, and use the second difference as a second face recognition difference corresponding to the multiple failed face recognitions in the face recognition failure data;

[0060] a third determining subunit, configured to use the first face recognition difference as a first type of difference set, and use a set consisting of the second face recognition difference as a second type of difference set;

[0061] The fourth determination subunit is used to determine the similarity coefficient between the second face recognition data and multiple failed face recognitions in the face recognition failure data based on the first type difference set and the second type difference set.

[0062] In a possible implementation manner, the fourth determining subunit is specifically configured to:

[0063] Determine the number of first differences included in the first type of difference set and the number of second differences included in the second type of difference set; and determine the minimum value of the first number of differences and the second number of differences;

[0064] Constructing a difference space according to the first difference value included in the first type of difference value set, the second difference value included in the second type of difference value set, and the minimum value; wherein the number of dimensions of the difference space is equal to the minimum value, and each dimension corresponds to a difference value;

[0065] Determine a plurality of first-class difference subsets of the first-class difference set; wherein the number of elements contained in the first-class difference subset is equal to the number of dimensions of the difference space; for each first-class difference subset, sort the elements of the first-class difference subset in ascending order, and correspond the elements to the dimensions of the difference space one by one in sequence; determine a first spatial point of the first-class difference subset in the difference space; wherein the value of the first spatial point in each dimension is equal to the element of the first-class difference subset corresponding to the dimension;

[0066] Determine a plurality of second-class difference subsets of the second-class difference set; wherein the number of elements contained in the second-class difference subset is equal to the number of dimensions of the difference space; for each second-class difference subset, sort the elements of the second-class difference subset in ascending order, and correspond the elements to the dimensions of the difference space one by one in sequence; determine a second spatial point of the second-class difference subset in the difference space; wherein the value of the second spatial point in each dimension is equal to the element of the second-class difference subset corresponding to the dimension;

[0067] In the difference space, determining the spatial distance between each first spatial point of the first type of difference subset and each second spatial point of the second type of difference subset;

[0068] Based on the minimum spatial distance among the spatial distances, a similarity coefficient between the second face recognition data and the multiple failed face recognitions in the face recognition failure data is determined.

[0069] In a possible implementation manner, the fourth determining subunit is specifically configured to:

[0070] determining a preset difference threshold;

[0071] The number of first face recognition difference values ​​less than the difference threshold in the first type of difference set is used as the first number of the first type of difference set; the number of face recognition difference values ​​greater than or equal to the difference threshold in the first type of difference set is used as the second number of the first type of difference set;

[0072] The number of face recognition difference values ​​less than the difference threshold in the second type of difference value set is used as the third number of the second type of difference value set; the number of face recognition difference values ​​greater than or equal to the difference threshold in the second type of difference value set is used as the fourth number of the second type of difference value set;

[0073] Based on the first number, the second number, the third number, and the fourth number, a similarity coefficient between the second face recognition data and multiple failed face recognitions in the face recognition failure data is determined.

[0074] In a possible implementation manner, the third determining unit is specifically configured to:

[0075] Get multiple preset potential cash thresholds;

[0076] For each potential cash threshold, determine target potential cash transaction data whose transaction amount is less than the potential cash threshold according to the transaction amount in the potential cash transaction data; and use the target potential cash transaction data as the potential cash transaction data of the potential cash threshold;

[0077] determining an abnormality coefficient of the potential cash threshold based on target potential cash transaction data of the potential cash threshold;

[0078] According to the abnormal coefficient of each potential cash threshold, the cash threshold corresponding to multiple failed face recognitions in the face recognition failure data is determined.

[0079] In a third aspect, an embodiment of the present application provides a server, including: a memory, a processor;

[0080] The memory stores computer-executable instructions;

[0081] The processor executes the computer-executable instructions stored in the memory, so that the processor executes the above first aspect and / or various possible implementations of the first aspect.

[0082] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the first aspect above and / or various possible implementations of the first aspect.

[0083] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above first aspect and / or various possible implementation methods of the first aspect.

[0084] The inter-bank transaction processing method, device, server and storage medium provided in the embodiment of the present application receive the first face recognition data sent by the preset inter-bank terminal. If it is determined that the first face recognition data includes face recognition failure data, multiple related terminals related to the inter-bank terminal are determined in the preset terminal set; wherein the face recognition failure data is the recognition data of the number of consecutive face recognition failures greater than the preset failure number threshold. According to the relevant terminal, the cash threshold corresponding to the face recognition failure data is determined, and the cash threshold is sent to the inter-bank terminal; wherein the cash threshold is used to control the amount of the target cash transaction of the inter-bank terminal after the face recognition is successful. In this scheme, if it is determined that the first face recognition data includes multiple consecutive face recognition failure data, multiple related terminals related to the inter-bank terminal are determined. According to the relevant terminal, the cash threshold corresponding to the face recognition failure data is determined, and the cash threshold is sent to the inter-bank terminal. After the inter-bank terminal receives the cash threshold, if the subsequent face recognition is successful, the amount of the target cash transaction of the inter-bank terminal is controlled according to the cash threshold. Therefore, after multiple consecutive facial recognition failures occur, the security of inter-bank transactions is greatly guaranteed by controlling the user's transaction amount, thereby achieving the effect of improving the security of inter-bank transactions. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0086] Figure 1 A schematic diagram of a cross-bank transaction processing method provided in an embodiment of the present application Figure 1 ;

[0087] Figure 2 Another cross-bank transaction processing method provided in the present application embodiment Figure 2 ;

[0088] Figure 3 A schematic diagram of the structure of an inter-bank transaction processing device provided in an embodiment of the present application;

[0089] Figure 4 A schematic diagram of the structure of another inter-bank transaction processing device provided in an embodiment of the present application;

[0090] Figure 5 A schematic diagram of the structure of a server provided in an embodiment of the present application.

[0091] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0092] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0093] Currently, the most widespread form of transaction is cash circulation, so in order to reduce transaction risks, cash transactions need to be controlled.

[0094] In one example, a user can deposit and withdraw money at a terminal to realize money circulation. However, in the prior art, if a user operates at an inter-bank terminal, the security of inter-bank cash transactions is low due to the lack of risk information sharing between inter-bank systems.

[0095] In combination with the above scenarios, it can be seen that in the prior art, the traditional inter-bank system lacks a security mechanism, and there is a technical problem of low security in inter-bank transactions.

[0096] The inter-bank transaction processing method provided by the present application greatly ensures the security of inter-bank transactions by controlling the user's transaction amount after multiple consecutive facial recognition failures, and solves the technical problem of low security of inter-bank transactions.

[0097] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0098] Figure 1 A schematic diagram of a cross-bank transaction processing method provided for this application Figure 1 ,like Figure 1 As shown, the method includes:

[0099] S101: Receive first face recognition data sent by a preset inter-bank terminal.

[0100] For example, the execution subject of this embodiment may be a server, or a terminal device, or an inter-bank transaction processing device or device, or other devices or equipment that can execute this embodiment, and there is no limitation to this. In this embodiment, the execution subject is introduced as a server.

[0101] First, the server corresponds to a preset interbank terminal for transactions, and the user can perform a face recognition operation at the interbank terminal. The interbank terminal generates first face recognition data in response to the face recognition operation and sends the first face recognition data to the server. The server receives the first face recognition data sent by the interbank terminal, wherein the first face recognition data may include recognition data of failed face recognition, recognition data of successful face recognition, etc.

[0102] S102. If it is determined that the first face recognition data includes face recognition failure data, determine a plurality of related terminals related to the inter-bank terminal in a preset terminal set; wherein the face recognition failure data is recognition data in which the number of consecutive face recognition failures is greater than a preset failure number threshold.

[0103] Exemplarily, it is detected whether the first face recognition data includes face recognition failure data, and face recognition failure data is recognition data in which the number of consecutive face recognition failures is greater than a preset failure number threshold; for example, the failure number threshold is 3, and it is detected whether the first face recognition data includes recognition data in which face recognition fails three times in a row. If it is determined that the first face recognition data includes face recognition failure data, multiple related terminals related to inter-bank terminals are determined in a preset terminal set; wherein the preset terminal set includes multiple terminals, and the multiple terminals may include other inter-bank terminals, or include terminals that do not inter-bank, or include other inter-bank terminals and terminals that do not inter-bank, and this is not limited.

[0104] S103. Determine the cash threshold corresponding to the face recognition failure data according to the relevant terminal, and send the cash threshold to the inter-bank terminal; wherein the cash threshold is used to control the amount of the target cash transaction of the inter-bank terminal after the face recognition is successful.

[0105] Exemplarily, the cash threshold corresponding to the face recognition failure data is determined according to the relevant terminal, and the cash threshold is sent to the inter-bank terminal. After the inter-bank terminal receives the cash threshold, if the user's subsequent face recognition is successful, the amount of the target cash transaction of the inter-bank terminal is controlled according to the cash threshold.

[0106] The inter-bank transaction processing method provided in the embodiment of the present application receives the first face recognition data sent by the preset inter-bank terminal. If it is determined that the first face recognition data includes face recognition failure data, multiple related terminals related to the inter-bank terminal are determined in the preset terminal set; wherein the face recognition failure data is the recognition data of the number of consecutive face recognition failures greater than the preset failure number threshold. According to the relevant terminal, the cash threshold corresponding to the face recognition failure data is determined, and the cash threshold is sent to the inter-bank terminal; wherein the cash threshold is used to control the amount of the target cash transaction of the inter-bank terminal after the face recognition is successful. In this scheme, if it is determined that the first face recognition data includes multiple consecutive face recognition failure data, multiple related terminals related to the inter-bank terminal are determined. According to the relevant terminal, the cash threshold corresponding to the face recognition failure data is determined, and the cash threshold is sent to the inter-bank terminal. After the inter-bank terminal receives the cash threshold, if the subsequent face recognition is successful, the amount of the target cash transaction of the inter-bank terminal is controlled according to the cash threshold. Therefore, after multiple consecutive facial recognition failures occur, the security of inter-bank transactions is greatly guaranteed by controlling the user's transaction amount, thereby achieving the effect of improving the security of inter-bank transactions.

[0107] Figure 2 A schematic diagram of a cross-bank transaction processing method provided for this application Figure 2 ,like Figure 2 As shown, in this embodiment Figure 1 Based on the embodiment, a method for processing inter-bank transactions is described in detail, and the method includes:

[0108] S201: Receive first face recognition data sent by a preset inter-bank terminal.

[0109] For example, this step can be referred to Figure 1 Step 101 in the above is not described in detail.

[0110] S202. If it is determined that the first face recognition data includes face recognition failure data, a plurality of related terminals related to the inter-bank terminal are determined in a preset terminal set; wherein the face recognition failure data is recognition data in which the number of consecutive face recognition failures is greater than a preset failure number threshold.

[0111] In one example, step S202 includes: if it is determined that the first face recognition data includes face recognition failure data, then in a preset terminal set, the physical distance between each terminal in the terminal set and the inter-bank terminal is determined respectively; if it is determined that there is a target physical distance less than a preset physical distance threshold, the terminal corresponding to the target physical distance is used as the relevant terminal; if there is no target physical distance less than the physical distance threshold, the first category of merchants for each terminal in the terminal set and the second category of merchants for the inter-bank terminal are obtained; wherein the physical distance between each terminal and the first category of merchants is less than a preset first threshold, and the physical distance between the inter-bank terminal and the second category of merchants is less than a preset second threshold; based on the first category of merchants and the second category of merchants, multiple relevant terminals related to the inter-bank terminal are selected from the terminal set.

[0112] Exemplarily, the face recognition failure data is recognition data in which the number of consecutive face recognition failures is greater than a preset failure number threshold. If it is determined that the first face recognition data includes face recognition failure data, then in the preset terminal set, the physical distance between each terminal in the terminal set and the inter-bank terminal is determined respectively. It is determined whether there is a target physical distance less than the preset physical distance threshold among the physical distances. If it is determined that there is a target physical distance less than the physical distance threshold, the terminal corresponding to the target physical distance is used as the relevant terminal.

[0113] If there is no target physical distance less than the physical distance threshold, then determine the first category of merchants corresponding to each terminal in the terminal set, determine the physical distance between each terminal in the terminal set and the first category of merchants, and then determine the physical distance less than the preset first threshold and the first category of merchants with a physical distance less than the first threshold; and determine the second category of merchants corresponding to each terminal in the terminal set, determine the physical distance between each terminal in the terminal set and the second category of merchants, and then determine the physical distance less than the preset second threshold and the second category of merchants with a physical distance less than the second threshold; wherein the first threshold and the second threshold can be preset values ​​or user-defined values, which are not limited. Then, based on the first category of merchants and the second category of merchants, select multiple related terminals related to inter-bank terminals in the terminal set.

[0114] Furthermore, when multiple related terminals related to inter-bank terminals are selected from the terminal set, multiple payment dimensions can be obtained and sorted. Determine the dimension values ​​of each payment dimension corresponding to the historical customer payment data of the first category of merchants, and sort the dimension values ​​of each payment dimension corresponding to the historical customer payment data of the first category of merchants according to the sorting of multiple payment dimensions, and construct the latitude values ​​into a first category of vectors according to the sorting results. Determine the dimension values ​​of each payment dimension corresponding to the historical customer payment data of the second category of merchants, and sort the dimension values ​​of each payment dimension corresponding to the historical customer payment data of the second category of merchants according to the sorting of multiple payment dimensions, and construct the latitude values ​​into a second category of vectors according to the sorting results. Finally, the distance between the first category vector and the second category vector is used as the dimensional distance between the corresponding terminal and the inter-bank terminal, and based on the dimensional distance, multiple related terminals are selected from multiple terminals in the terminal set of the server.

[0115] S203, obtaining multiple second face recognition data and cash transaction data of multiple related terminals; wherein each second face recognition data includes multiple consecutive failed face recognitions and successful face recognitions after failures.

[0116] Exemplarily, the second face recognition data and cash transaction data of each relevant terminal are obtained, wherein each second face recognition data includes multiple consecutive failed face recognitions and successful face recognitions after the failures.

[0117] S204: Determine a similarity coefficient between each second face recognition data in the plurality of second face recognition data and a plurality of failed face recognitions in the face recognition failure data of the inter-bank terminal.

[0118] In one example, step S204 includes: for each failed face recognition included in the second face recognition data, determining a first difference between a first preset face recognition matching threshold corresponding to the failed face recognition and the first face recognition matching value, and using the first difference as the first face recognition difference of the second face recognition data; for each failed face recognition in multiple failed face recognitions in the face recognition failure data, determining a second difference between a second preset face recognition matching threshold for the failed face recognition and the second face recognition matching value, and using the second difference as the second face recognition difference corresponding to the multiple failed face recognitions in the face recognition failure data; using the first face recognition difference as a first type of difference set, and using the set consisting of the second face recognition difference as a second type of difference set; based on the first type of difference set and the second type of difference set, determining the similarity coefficient between the second face recognition data and the multiple failed face recognitions in the face recognition failure data.

[0119] In one example, “determining the similarity coefficient between the second face recognition data and the multiple failed face recognitions in the face recognition failure data based on the first difference set and the second difference set” includes two implementation methods:

[0120] A first implementation of step 204: determining the number of first differences included in the first-class difference set and the number of second differences included in the second-class difference set; and determining the minimum value of the first difference number and the second difference number; constructing a difference space according to the first difference included in the first-class difference set, the second difference included in the second-class difference set, and the minimum value; wherein the number of dimensions of the difference space is equal to the minimum value, and each dimension corresponds to a difference; determining multiple first-class difference subsets of the first-class difference set; wherein the number of elements included in the first-class difference subset is equal to the number of dimensions of the difference space; for each first-class difference subset, sorting the elements of the first-class difference subset in order from small to large, and corresponding the elements to the dimensions of the difference space one by one in sequence; determining a first spatial point of the first-class difference subset in the difference space; wherein the first spatial point in each dimension The value of is equal to the element of the first type of difference subset corresponding to the dimension; determine multiple second type of difference subsets of the second type of difference set; wherein the number of elements contained in the second type of difference subset is equal to the number of dimensions of the difference space; for each second type of difference subset, sort the elements of the second type of difference subset in order from small to large, and correspond the elements to the dimensions of the difference space one by one; determine the second spatial point of the second type of difference subset in the difference space; wherein the value of the second spatial point in each dimension is equal to the element of the second type of difference subset corresponding to the dimension; in the difference space, determine the spatial distance between each first spatial point of the first type of difference subset and each second spatial point of the second type of difference subset; based on the minimum spatial distance in the spatial distances, determine the similarity coefficient between the second face recognition data and the multiple failed face recognitions in the face recognition failure data.

[0121] A second implementation method of step 204: determine a preset difference threshold; use the number of first face recognition differences less than the difference threshold in the first category difference set as the first number of the first category difference set; use the number of face recognition differences greater than or equal to the difference threshold in the first category difference set as the second number of the first category difference set; use the number of face recognition differences less than the difference threshold in the second category difference set as the third number of the second category difference set; use the number of face recognition differences greater than or equal to the difference threshold in the second category difference set as the fourth number of the second category difference set; determine the similarity coefficient between the second face recognition data and the multiple failed face recognitions in the face recognition failure data based on the first number, the second number, the third number, and the fourth number.

[0122] Exemplarily, for each failed face recognition included in the second face recognition data, the first difference between the first preset face recognition matching threshold corresponding to the failed face recognition and the first face recognition matching value is determined, and the first difference is used as the first face recognition difference of the second face recognition data, wherein the first face recognition matching value is the actual matching value of the user. For each failed face recognition in the multiple failed face recognitions of the face recognition failure data, the second difference between the second preset face recognition matching threshold of the failed face recognition and the second face recognition matching value is determined, and the second difference is used as the second face recognition difference corresponding to the multiple failed face recognitions in the face recognition failure data, and the second face recognition matching value is the actual matching value of the user. The first face recognition difference is used as a first-class difference set, and the set composed of the second face recognition difference is used as a second-class difference set. Based on the first-class difference set and the second-class difference set, the similarity coefficient between the second face recognition data and the multiple failed face recognitions in the face recognition failure data is comprehensively determined.

[0123] Specifically, in a first implementation method of determining a similarity coefficient, the number of first differences contained in a first-class difference set and the number of second differences contained in a second-class difference set are determined; wherein the first-class difference set contains multiple first differences. The minimum value of the number of first differences and the number of second differences is determined, and a difference space is constructed according to the first differences contained in the first-class difference set, the second differences contained in the second-class difference set, and the minimum value; wherein the number of dimensions of the difference space is equal to the minimum value, and each dimension corresponds to a difference. Multiple first-class difference subsets of the first-class difference set are determined; wherein the number of elements contained in the first-class difference subset is equal to the number of dimensions of the difference space; and for each first-class difference subset, the elements of the first-class difference subset are sorted in order from small to large, and the elements are sequentially matched one-to-one with the dimensions of the difference space. Determine the first spatial point of the first difference subset in the difference space; wherein the value of the first spatial point in each dimension is equal to the element of the first difference subset corresponding to the dimension; determine multiple second difference subsets of the second difference set; wherein the number of elements contained in the second difference subset is equal to the number of dimensions of the difference space; for each second difference subset, sort the elements of the second difference subset in order from small to large, and correspond the elements to the dimensions of the difference space one by one. Determine the second spatial point of the second difference subset in the difference space; wherein the value of the second spatial point in each dimension is equal to the element of the second difference subset corresponding to the dimension. In the difference space, determine the spatial distance between each first spatial point of the first difference subset and each second spatial point of the second difference subset, and determine the similarity coefficient between the second face recognition data and the multiple failed face recognitions in the face recognition failure data based on the minimum spatial distance in the spatial distances.

[0124] In a second implementation method for determining a similarity coefficient, a preset difference threshold is determined; the number of first face recognition differences less than the difference threshold in a first category difference set is used as the first number of the first category difference set; the number of face recognition differences greater than or equal to the difference threshold in the first category difference set is used as the second number of the first category difference set; the number of face recognition differences less than the difference threshold in a second category difference set is used as the third number of the second category difference set; the number of face recognition differences greater than or equal to the difference threshold in the second category difference set is used as the fourth number of the second category difference set; and based on the first number, the second number, the third number, and the fourth number, the similarity coefficient between the second face recognition data and the multiple failed face recognitions in the face recognition failure data is determined.

[0125] S205. Use the second face recognition data corresponding to the similarity coefficient greater than the preset similarity threshold as the similar face recognition data, and use the cash transaction data executed after the similar face recognition data as the potential cash transaction data.

[0126] Exemplarily, a similarity coefficient greater than a preset similarity threshold is determined, the second face recognition data corresponding to the similarity coefficient greater than the similarity threshold is used as similar face recognition data, and the cash transaction data executed after the similar face recognition data is used as potential cash transaction data.

[0127] S206. Determine a cash threshold corresponding to multiple failed face recognitions in the face recognition failure data based on the potential cash transaction data.

[0128] In one example, step S206 includes: obtaining multiple preset potential cash thresholds; for each potential cash threshold, determining target potential cash transaction data whose transaction amount is less than the potential cash threshold based on the transaction amount in the potential cash transaction data; and using the target potential cash transaction data as the potential cash transaction data of the potential cash threshold; determining the abnormality coefficient of the potential cash threshold based on the target potential cash transaction data of the potential cash threshold; and determining the cash threshold corresponding to multiple failed face recognitions in the face recognition failure data based on the abnormality coefficients of each potential cash threshold.

[0129] Exemplarily, multiple preset potential cash thresholds are obtained; wherein, the multiple preset potential cash thresholds may be automatically set by the server, may be user-defined, or may be automatically set according to the transaction amount in the cash transaction data, and there is no limitation on this. The potential cash transaction data includes multiple transaction amounts, and for each potential cash threshold, according to the transaction amount in the potential cash transaction data, in the potential cash transaction data, target potential cash transaction data with a transaction amount less than the potential cash threshold is determined, and the target potential cash transaction data is used as the potential cash transaction data of the potential cash threshold. Based on the target potential cash transaction data of the potential cash threshold, the abnormal coefficient of the potential cash threshold is determined, and finally, based on the abnormal coefficient of each potential cash threshold, the cash threshold corresponding to multiple failed face recognitions in the face recognition failure data is determined.

[0130] Furthermore, based on the abnormality coefficient of each potential cash threshold, the cash threshold corresponding to the multiple failed face recognitions in the face recognition failure data is determined, including: obtaining the cash threshold set by the customer on the first bank server; determining the abnormality coefficient of the cash threshold based on the cash transaction data of the first bank server; selecting multiple potential cash thresholds whose corresponding abnormality coefficients are less than or equal to the abnormality coefficient of the cash threshold from each potential cash threshold; and using the potential cash threshold with the largest value among the selected multiple potential cash thresholds as the cash threshold corresponding to the multiple failed face recognitions.

[0131] S207. Send the cash threshold to the inter-bank terminal; wherein the cash threshold is used to control the amount of the target cash transaction of the inter-bank terminal after the face recognition is successful.

[0132] Exemplarily, the cash threshold is sent to the inter-bank terminal. After the inter-bank terminal receives the cash threshold, if the user's subsequent face recognition is successful, the amount of the target cash transaction of the inter-bank terminal is controlled according to the cash threshold.

[0133] The inter-bank transaction processing method provided in the embodiment of the present application receives the first face recognition data sent by the preset inter-bank terminal. If it is determined that the first face recognition data includes face recognition failure data, multiple related terminals related to the inter-bank terminal are determined in the preset terminal set; wherein the face recognition failure data is the recognition data of the number of consecutive face recognition failures greater than the preset failure number threshold. Multiple second face recognition data and cash transaction data of multiple related terminals are obtained; wherein each second face recognition data includes multiple consecutive failed face recognitions and successful face recognition after failure. Determine the similarity coefficient between each second face recognition data in the multiple second face recognition data and the multiple failed face recognitions in the face recognition failure data of the inter-bank terminal. The second face recognition data corresponding to the similarity coefficient greater than the preset similarity threshold is used as the similar face recognition data, and the cash transaction data executed after the similar face recognition data is used as the potential cash transaction data. Based on the potential cash transaction data, the cash threshold corresponding to the multiple failed face recognitions in the face recognition failure data is determined. The cash threshold is sent to the inter-bank terminal; the cash threshold is used to control the target cash transaction amount of the inter-bank terminal after the face recognition is successful. Therefore, after multiple consecutive face recognition failures, by controlling the user's transaction amount, the security of the inter-bank transaction is greatly guaranteed, so as to achieve the effect of improving the security of the inter-bank transaction.

[0134] Based on the above embodiments, in one embodiment, the inter-bank transaction processing method further includes:

[0135] Acquire multiple cash transaction data of multiple related cash terminals; determine a cash transaction anomaly model based on the multiple cash transaction data, wherein the cash transaction anomaly model is used to determine whether there is an anomaly in the cash transaction. According to the cash transaction anomaly model, control the cash transaction after successful face recognition. For example, when it is determined that there is an anomaly in the cash transaction, perform other identity verification on the customer in addition to face recognition.

[0136] Specifically, determining a cash transaction abnormality model based on the multiple cash transaction data includes:

[0137] For each of the multiple cash transaction data, multiple candidate cash attribute sets corresponding to the cash transaction data are determined, wherein each candidate cash attribute set includes multiple cash attribute pairs, and each cash attribute pair includes a cash attribute and a corresponding attribute value. Abnormal cash transaction data involving abnormalities are selected from the multiple cash transaction data, and the abnormal proportions of each candidate cash attribute set corresponding to each abnormal cash transaction data are determined. Then, the candidate cash attribute set with the largest abnormal proportion in each candidate cash attribute set corresponding to the abnormal cash transaction data is taken as the abnormal cash attribute set, and the corresponding abnormal proportion is taken as the abnormal proportion corresponding to the abnormal cash attribute set. Finally, a set consisting of abnormal cash attribute sets whose corresponding abnormal proportions are greater than a set value is taken as a cash transaction abnormality model.

[0138] In one embodiment, determining the abnormal proportion of each candidate cash attribute set corresponding to each abnormal cash transaction data includes: for each candidate cash attribute set corresponding to the abnormal cash transaction data, determining multiple candidate cash attribute sets 1 including the candidate cash attribute set among the multiple cash transaction data, determining cash transaction data 1 corresponding to the multiple candidate cash attribute sets 1, and using cash transaction data 1 as cash transaction data of the candidate cash attribute set; and using the abnormal cash transaction data including the candidate cash attribute set among the multiple candidate cash attribute sets corresponding to the abnormal cash transaction data as the abnormal cash transaction data of the candidate cash attribute set. Finally, determining the number of cash transactions included in the abnormal cash transaction data of the candidate cash attribute set and the number of cash transactions included in the cash transaction data of the candidate cash attribute set, and using the ratio of the two cash transaction numbers as the abnormal proportion of the candidate cash attribute set.

[0139] Figure 3 A schematic diagram of the structure of an inter-bank transaction processing device provided in this application, such as Figure 3 As shown, the inter-bank transaction processing device 30 provided in this embodiment includes:

[0140] The receiving module 31 is used to receive first face recognition data sent by a preset inter-bank terminal.

[0141] The first determination module 32 is used to determine a plurality of related terminals related to the inter-bank terminal in a preset terminal set if it is determined that the first face recognition data includes face recognition failure data; wherein the face recognition failure data is recognition data in which the number of consecutive face recognition failures is greater than a preset failure number threshold.

[0142] The second determination module 33 is used to determine the cash threshold corresponding to the face recognition failure data according to the relevant terminal.

[0143] The sending module 34 is used to send the cash threshold to the inter-bank terminal; wherein the cash threshold is used to control the amount of the target cash transaction of the inter-bank terminal after the face recognition is successful.

[0144] Figure 4 A schematic diagram of the structure of another inter-bank transaction processing device provided in an embodiment of the present application, Figure 3 Based on the embodiment shown, Figure 4 As shown, the first determination module 32 is specifically used for:

[0145] If it is determined that the first face recognition data includes face recognition failure data, then in the preset terminal set, the physical distance between each terminal in the terminal set and the inter-bank terminal is determined respectively.

[0146] If it is determined that there is a target physical distance that is less than a preset physical distance threshold, the terminal corresponding to the target physical distance is used as a related terminal.

[0147] If there is no target physical distance less than the physical distance threshold, the first category of merchants for each terminal in the terminal set and the second category of merchants for inter-bank terminals are obtained; wherein the physical distance between each terminal and the first category of merchants is less than a preset first threshold, and the physical distance between the inter-bank terminal and the second category of merchants is less than a preset second threshold.

[0148] According to the first category of merchants and the second category of merchants, a plurality of related terminals related to the inter-bank terminal are selected from the terminal set.

[0149] In a possible implementation, the second determining module 33 includes:

[0150] The acquisition unit 331 is used to acquire multiple second face recognition data and cash transaction data of the relevant terminal; wherein each second face recognition data includes multiple consecutive failed face recognitions and successful face recognitions after failures.

[0151] The first determining unit 332 is used to determine a similarity coefficient between each second face recognition data in the plurality of second face recognition data and a plurality of failed face recognitions in the face recognition failure data of the inter-bank terminal.

[0152] The second determination unit 333 is used to use the second face recognition data corresponding to the similarity coefficient greater than the preset similarity threshold as the similar face recognition data, and use the cash transaction data executed after the similar face recognition data as the potential cash transaction data.

[0153] The third determination unit 334 is used to determine a cash threshold corresponding to multiple failed face recognitions in the face recognition failure data based on the potential cash transaction data.

[0154] In a possible implementation, the first determining unit 332 includes:

[0155] The first determining subunit 3321 is used to determine, for each failed face recognition included in the second face recognition data, a first difference between a first preset face recognition matching threshold corresponding to the failed face recognition and the first face recognition matching value, and use the first difference as a first face recognition difference value of the second face recognition data;

[0156] The second determination subunit 3322 is used to determine, for each failed face recognition in the multiple failed face recognitions in the face recognition failure data, a second difference between a second preset face recognition matching threshold and a second face recognition matching value of the failed face recognition, and use the second difference as a second face recognition difference corresponding to the multiple failed face recognitions in the face recognition failure data.

[0157] The third determining subunit 3323 is used to use the first face recognition difference as a first type of difference set, and use the set composed of the second face recognition difference as a second type of difference set.

[0158] The fourth determining subunit 3324 is used to determine the similarity coefficient between the second face recognition data and the multiple failed face recognitions in the face recognition failure data based on the first type of difference set and the second type of difference set.

[0159] In a possible implementation, the fourth determining subunit 3324 is specifically configured to:

[0160] Determine the number of first differences included in the first type of difference set and the number of second differences included in the second type of difference set; and determine the minimum value of the first difference number and the second difference number.

[0161] A difference space is constructed according to the first difference values ​​included in the first type of difference value set, the second difference values ​​included in the second type of difference value set, and the minimum value; wherein the number of dimensions of the difference space is equal to the minimum value, and each dimension corresponds to a difference value.

[0162] Determine multiple first-class difference subsets of the first-class difference set; wherein the number of elements contained in the first-class difference subset is equal to the number of dimensions of the difference space; for each first-class difference subset, sort the elements of the first-class difference subset in order from small to large, and correspond the elements to the dimensions of the difference space one by one; determine the first spatial point of the first-class difference subset in the difference space; wherein the value of the first spatial point in each dimension is equal to the element of the first-class difference subset corresponding to the dimension.

[0163] Determine multiple second-class difference subsets of the second-class difference set; wherein the number of elements contained in the second-class difference subset is equal to the number of dimensions of the difference space; for each second-class difference subset, sort the elements of the second-class difference subset in order from small to large, and correspond the elements to the dimensions of the difference space one by one in turn; determine the second space point of the second-class difference subset in the difference space; wherein the value of the second space point in each dimension is equal to the element of the second-class difference subset corresponding to the dimension.

[0164] In the difference space, the spatial distance between each first spatial point of the first type of difference subset and each second spatial point of the second type of difference subset is determined.

[0165] Based on the minimum spatial distance in the spatial distances, a similarity coefficient between the second face recognition data and the multiple failed face recognitions in the face recognition failure data is determined.

[0166] In a possible implementation, the fourth determining subunit 3324 is specifically configured to:

[0167] Determine the preset difference threshold.

[0168] The number of first face recognition difference values ​​less than the difference threshold in the first category difference set is taken as the first number of the first category difference set; the number of face recognition difference values ​​greater than or equal to the difference threshold in the first category difference set is taken as the second number of the first category difference set.

[0169] The number of face recognition difference values ​​in the second category difference set that are less than the difference threshold is taken as the third number of the second category difference set; the number of face recognition difference values ​​in the second category difference set that are greater than or equal to the difference threshold is taken as the fourth number of the second category difference set.

[0170] A similarity coefficient between the second face recognition data and the multiple failed face recognitions in the face recognition failure data is determined based on the first number, the second number, the third number, and the fourth number.

[0171] In a possible implementation manner, the third determining unit 334 is specifically configured to:

[0172] Get multiple preset potential cash thresholds.

[0173] For each potential cash threshold, target potential cash transaction data whose transaction amount is less than the potential cash threshold is determined according to the transaction amount in the potential cash transaction data; and the target potential cash transaction data is used as the potential cash transaction data of the potential cash threshold.

[0174] An abnormality coefficient of the potential cash threshold is determined based on target potential cash transaction data of the potential cash threshold.

[0175] According to the abnormal coefficient of each potential cash threshold, the cash threshold corresponding to multiple failed face recognitions in the face recognition failure data is determined.

[0176] The device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and this embodiment will not be described in detail here.

[0177] Figure 5 This is a schematic diagram of the server structure provided for this application. Figure 5 As shown, the server 40 provided in this embodiment includes: at least one processor 401 and a memory 402. Optionally, the server 40 also includes a communication component 403. The processor 401, the memory 402 and the communication component 403 are connected via a bus 404.

[0178] In a specific implementation process, at least one processor 401 executes the computer-executable instructions stored in the memory 402, so that at least one processor 401 executes the above method.

[0179] The specific implementation process of the processor 401 can be found in the above method embodiment, and its implementation principle and technical effect are similar, so this embodiment will not be repeated here.

[0180] In the above embodiments, it should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the invention may be directly implemented as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

[0181] The memory may include a high-speed memory (Random Access Memory, RAM), and may also include a non-volatile memory (Non-volatile Memory, NVM), such as at least one disk storage.

[0182] The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of the present application is not limited to only one bus or one type of bus.

[0183] The present application also provides a computer program product, including a computer program, which implements the above method when executed by a processor.

[0184] The present application also provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the above method is implemented.

[0185] The above-mentioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general or special-purpose computer.

[0186] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (Application Specific Integrated Circuits, referred to as: ASIC). Of course, the processor and the readable storage medium can also exist in the device as discrete components.

[0187] The division of units is only a logical function division, and there may be other divisions in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0188] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0189] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0190] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0191] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk and other media that can store program codes.

[0192] Finally, it should be noted that those skilled in the art will readily conceive of other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses or adaptations of the present invention, which follow the general principles of the present invention and include common knowledge or customary technical means in the art not disclosed by the present invention, are not limited to the precise structure described above and shown in the drawings, and may be modified and changed in various ways without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A method for processing inter-bank transactions, characterized in that: include: Receiving first face recognition data sent by a preset inter-bank terminal; If it is determined that the first face recognition data includes face recognition failure data, then in a preset terminal set, a plurality of related terminals related to the inter-bank terminal are determined; wherein the face recognition failure data is recognition data in which the number of consecutive face recognition failures is greater than a preset failure number threshold; According to the relevant terminal, a cash threshold corresponding to the face recognition failure data is determined, and the cash threshold is sent to the inter-bank terminal; wherein the cash threshold is used to control the amount of the target cash transaction of the inter-bank terminal after the face recognition is successful.

2. The method according to claim 1, characterized in that If it is determined that the first face recognition data includes face recognition failure data, a plurality of related terminals related to the inter-bank terminal are determined in a preset terminal set, including: If it is determined that the first face recognition data includes face recognition failure data, then, in a preset terminal set, respectively determining a physical distance between each terminal in the terminal set and the inter-bank terminal; If it is determined that there is a target physical distance that is less than the preset physical distance threshold, the terminal corresponding to the target physical distance is used as the relevant terminal; If there is no target physical distance less than the physical distance threshold, obtaining the first category of merchants of each terminal in the terminal set and the second category of merchants of the inter-bank terminal; wherein the physical distance between each terminal and the first category of merchants is less than a preset first threshold, and the physical distance between the inter-bank terminal and the second category of merchants is less than a preset second threshold; According to the first category of merchants and the second category of merchants, a plurality of related terminals related to the inter-bank terminal are selected from the terminal set.

3. The method according to claim 1, characterized in that The determining, according to the relevant terminal, a cash threshold corresponding to the face recognition failure data includes: Acquire multiple second face recognition data and cash transaction data of the relevant terminal; wherein each second face recognition data includes multiple consecutive failed face recognitions and successful face recognitions after the failures; Determine a similarity coefficient between each second face recognition data in the plurality of second face recognition data and a plurality of failed face recognitions in the face recognition failure data of the inter-bank terminal; The second face recognition data corresponding to the similarity coefficient greater than the preset similarity threshold is used as the similar face recognition data, and the cash transaction data executed after the similar face recognition data is used as the potential cash transaction data; A cash threshold corresponding to multiple failed face recognitions in the face recognition failure data is determined based on the potential cash transaction data.

4. The method according to claim 3, characterized in that The determining of the similarity coefficient between each of the plurality of second face recognition data and the plurality of failed face recognitions in the face recognition failure data of the inter-bank terminal includes: For each failed face recognition included in the second face recognition data, determine a first difference between a first preset face recognition matching threshold corresponding to the failed face recognition and a first face recognition matching value, and use the first difference as a first face recognition difference value of the second face recognition data; For each failed face recognition in the multiple failed face recognitions of the face recognition failure data, determine a second difference between a second preset face recognition matching threshold of the failed face recognition and a second face recognition matching value, and use the second difference as a second face recognition difference corresponding to the multiple failed face recognitions in the face recognition failure data; Taking the first face recognition difference as a first type of difference set, and taking the set consisting of the second face recognition difference as a second type of difference set; Based on the first type of difference set and the second type of difference set, a similarity coefficient between the second face recognition data and multiple failed face recognitions in the face recognition failure data is determined.

5. The method according to claim 4, characterized in that The determining, based on the first type of difference set and the second type of difference set, a similarity coefficient between the second face recognition data and a plurality of failed face recognitions in the face recognition failure data comprises: Determine the number of first differences included in the first type of difference set and the number of second differences included in the second type of difference set; and determine the minimum value of the first number of differences and the second number of differences; Constructing a difference space according to the first difference value included in the first type of difference value set, the second difference value included in the second type of difference value set, and the minimum value; wherein the number of dimensions of the difference space is equal to the minimum value, and each dimension corresponds to a difference value; Determine a plurality of first-class difference subsets of the first-class difference set; wherein the number of elements contained in the first-class difference subset is equal to the number of dimensions of the difference space; for each first-class difference subset, sort the elements of the first-class difference subset in ascending order, and correspond the elements to the dimensions of the difference space one by one in sequence; determine a first spatial point of the first-class difference subset in the difference space; wherein the value of the first spatial point in each dimension is equal to the element of the first-class difference subset corresponding to the dimension; Determine a plurality of second-class difference subsets of the second-class difference set; wherein the number of elements contained in the second-class difference subset is equal to the number of dimensions of the difference space; for each second-class difference subset, sort the elements of the second-class difference subset in ascending order, and correspond the elements to the dimensions of the difference space one by one in sequence; determine a second spatial point of the second-class difference subset in the difference space; wherein the value of the second spatial point in each dimension is equal to the element of the second-class difference subset corresponding to the dimension; In the difference space, determining the spatial distance between each first spatial point of the first type of difference subset and each second spatial point of the second type of difference subset; Based on the minimum spatial distance among the spatial distances, a similarity coefficient between the second face recognition data and the multiple failed face recognitions in the face recognition failure data is determined.

6. The method according to claim 4, characterized in that The determining, based on the first type of difference set and the second type of difference set, a similarity coefficient between the second face recognition data and a plurality of failed face recognitions in the face recognition failure data comprises: determining a preset difference threshold; The number of first face recognition difference values ​​less than the difference threshold in the first type of difference set is used as the first number of the first type of difference set; the number of face recognition difference values ​​greater than or equal to the difference threshold in the first type of difference set is used as the second number of the first type of difference set; The number of face recognition difference values ​​less than the difference threshold in the second type of difference value set is used as the third number of the second type of difference value set; the number of face recognition difference values ​​greater than or equal to the difference threshold in the second type of difference value set is used as the fourth number of the second type of difference value set; Based on the first number, the second number, the third number, and the fourth number, a similarity coefficient between the second face recognition data and multiple failed face recognitions in the face recognition failure data is determined.

7. The method according to any one of claims 3 to 6, characterized in that: The determining, based on the potential cash transaction data, a cash threshold corresponding to multiple failed face recognitions in the face recognition failure data includes: Get multiple preset potential cash thresholds; For each potential cash threshold, determining target potential cash transaction data whose transaction amount is less than the potential cash threshold according to the transaction amount in the potential cash transaction data; and using the target potential cash transaction data as the potential cash transaction data of the potential cash threshold; determining an abnormality coefficient of the potential cash threshold according to target potential cash transaction data of the potential cash threshold; The cash thresholds corresponding to the multiple failed face recognitions in the face recognition failure data are determined according to the abnormal coefficients of the potential cash thresholds.

8. An inter-bank transaction processing device, characterized in that: include: A receiving module, used for receiving first face recognition data sent by a preset inter-bank terminal; A first determination module, configured to determine, if it is determined that the first face recognition data includes face recognition failure data, a plurality of related terminals related to the inter-bank terminal in a preset terminal set; wherein the face recognition failure data is recognition data in which the number of consecutive face recognition failures is greater than a preset failure number threshold; A second determination module is used to determine the cash threshold corresponding to the face recognition failure data according to the relevant terminal; A sending module is used to send the cash threshold to the inter-bank terminal; wherein the cash threshold is used to control the amount of the target cash transaction of the inter-bank terminal after the face recognition is successful.

9. A server, characterized in that: include: Memory, processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory, so that the processor performs the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 7 when executed by a processor.

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