Fingerprint collision determination method and apparatus, storage medium, and electronic device

Abstract

The present disclosure relates to the technical field of computer, in particular to a fingerprint collision determination method, a fingerprint collision determination device, a storage medium and an electronic device. The fingerprint collision determination method comprises: obtaining identification information of a target recovered device, and determining a fingerprint collision characteristic value of the target recovered device according to the identification information, wherein the target recovered device is any one of the recovered devices; obtaining sensor parameters collected by a sensor of the target recovered device; determining a user operation behavior characteristic value of the recovered device according to the sensor parameters; and determining whether the target recovered device has a fingerprint collision according to the fingerprint collision characteristic value and the user operation behavior characteristic value. The present disclosure automatically detects whether the device has a fingerprint collision in time.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of computer, and particularly relates to a fingerprint collision determination method, a fingerprint collision determination device, a computer readable storage medium and an electronic device. BACKGROUND

[0002] Device fingerprint refers to device dimension information collected by a software toolkit, and specifically refers to device features or device identifiers that can be used to uniquely identify the device. Fingerprint collision refers to two different devices being restored to the same device, i.e., different terminal devices have the same device parameters, such as device ID. Fingerprint collision greatly interferes with the identification of the true identity of the target device.

[0003] Currently, there is no good self-discovery mechanism for device fingerprint collision. Usually, only when the abnormal data of the device fingerprint affects the user's use and is perceived by the user, it can be determined that device fingerprint collision has occurred. However, the user's perception of device fingerprint collision is usually lagging behind, that is, the user only realizes it after the device fingerprint collision has existed for a period of time. Therefore, before being perceived by the user, the device fingerprint has already collided to a certain extent, which may affect the correctness of the business data.

[0004] It should be noted that the information disclosed in the above background section is only used to strengthen the understanding of the background of the present disclosure, and therefore can include information that does not constitute prior art known to those of ordinary skill in the art. SUMMARY

[0005] The purpose of the present disclosure is to provide a fingerprint collision determination method, a fingerprint collision determination device, a computer readable storage medium and an electronic device, thereby at least partially overcoming the problem of affecting the correctness of business data caused by determining device fingerprint collision by user query.

[0006] Other characteristics and advantages of the present disclosure will become apparent from the following detailed description, or will be learned by practice of the present disclosure.

[0007] According to a first aspect of the present disclosure, a fingerprint collision determination method is provided, comprising:

[0008] Obtaining identification information of a target restored device, and determining a fingerprint collision feature value of the target restored device according to the identification information, the target restored device being any one of the restored devices;

[0009] Obtaining a sensor parameter collected by a sensor of the target restored device;

[0010] Determining a user operation behavior feature value of the target restored device according to the sensor parameter;

[0011] determining whether the target restored device has a fingerprint collision according to the fingerprint collision characteristic value of the target restored device and the user operation behavior characteristic value of the target restored device.

[0012] In an example embodiment of the present disclosure, the identification information comprises a device ID of the target restored device and preset field information, and the determining the fingerprint collision characteristic value of the target restored device according to the identification information comprises:

[0013] If the device ID of the target restored device is consistent with that of other restored devices and the preset field information of the target restored device is inconsistent with that of the other restored devices, determining the fingerprint collision characteristic value of the target restored device as a first preset threshold value.

[0014] In an example embodiment of the present disclosure, the determining the fingerprint collision characteristic value of the target restored device according to the identification information further comprises:

[0015] If the device ID of the target restored device is consistent with that of other restored devices and the preset field information of the target restored device is consistent with that of the other restored devices, determining the fingerprint collision characteristic value of the target restored device as a second preset threshold value.

[0016] In an example embodiment of the present disclosure, the determining the user operation behavior characteristic value of the target restored device according to the sensor parameter comprises:

[0017] obtaining a plurality of first sensor parameters collected by the sensor within a first preset time length;

[0018] screening the plurality of first sensor parameters to obtain screened sensor parameters;

[0019] obtaining a first average value and a first variance value of the screened sensor parameters;

[0020] determining the user operation behavior characteristic value of the target restored device according to the first average value and the first variance value.

[0021] In an example embodiment of the present disclosure, the screening the plurality of first sensor parameters to obtain screened sensor parameters comprises:

[0022] obtaining second sensor parameters collected by the sensor within a second preset time length, the time sequence of the second preset time length being before the first preset time length;

[0023] obtaining a second average value and a second variance value of the second sensor parameters;

[0024] determine a screening interval according to the second average value and the second variance value;

[0025] determine a target sensor parameter in the screening interval from the first sensor parameter, and take the target sensor parameter as the screened sensor parameter.

[0026] In an example embodiment of the present disclosure, the determining whether the target recovered device has fingerprint collision according to the fingerprint collision feature value and the user operation behavior feature value comprises:

[0027] obtain a weighted sum value of the fingerprint collision feature value and the user operation behavior feature value;

[0028] if the weighted sum value is greater than or equal to a third preset threshold value, determine that the target recovered device has fingerprint collision;

[0029] if the weighted sum value is less than the third preset threshold value, determine that the target recovered device does not have fingerprint collision.

[0030] In an example embodiment of the present disclosure, before the obtaining the identification information of the recovered device, the method further comprises:

[0031] obtain a first device whose timestamp is within a third preset time length;

[0032] determine a second device recovered within a fourth preset time length from the first device, and take the second device as the recovered device, the fourth preset time length being adjacent to the third preset time length and in time sequence after the third preset time length.

[0033] According to a second aspect of the present disclosure, a fingerprint collision determination apparatus is provided, comprising:

[0034] a fingerprint collision feature value determination module, configured to obtain identification information of a target recovered device, and determine a fingerprint collision feature value of the target recovered device according to the identification information, the target recovered device being any one of the recovered devices;

[0035] a sensor parameter obtaining module, configured to obtain a sensor parameter collected by a sensor of the target recovered device;

[0036] a user operation behavior feature value obtaining module, configured to determine a user operation behavior feature value of the target recovered device according to the sensor parameter;

[0037] a fingerprint collision determination module, configured to determine whether the target recovered device has fingerprint collision according to the fingerprint collision feature value and the user operation behavior feature value.

[0038] According to a third aspect of the present disclosure, a computer readable storage medium is provided, which stores a computer program, the program being executed by a processor to implement the steps of the method of any one of the first aspect.

[0039] According to a fourth aspect of the present disclosure, an electronic device is provided, comprising:

[0040] a processor; and

[0041] a memory for storing executable instructions of the processor;

[0042] wherein the processor is configured to execute the steps of the method of the first aspect via executing the executable instructions.

[0043] The technical solutions provided by the embodiments of the present disclosure can include the following beneficial effects:

[0044] To sum up, the method provided by the present disclosure can acquire the identification information of the target recovered device, and determine the fingerprint collision characteristic value of the target recovered device according to the identification information; acquire the sensor parameter of the target recovered device; determine the user operation behavior characteristic value of the target recovered device according to the sensor parameter; and determine whether the target recovered device has fingerprint collision according to the fingerprint collision characteristic value and the user operation behavior characteristic value. The method can automatically detect whether the device has fingerprint collision in time, avoid the lag problem of determining that the device fingerprint has collision by using the user query method, is conducive to improving the correctness of the business data, and greatly improves the user experience.

[0045] It should be understood that the foregoing general description and the following detailed description are only exemplary and explanatory, and are not limiting to the present disclosure. BRIEF DESCRIPTION OF DRAWINGS

[0046] The accompanying drawings, which are incorporated into and form part of the specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the disclosure. Obviously, the drawings in the following description are only some embodiments of the present disclosure, and other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0047] Figure 1 The flowchart of a fingerprint collision determination method in an exemplary embodiment of the present disclosure is schematically shown Figure 1 ;

[0048] Figure 2 The architecture diagram of a fingerprint collision determination system in an exemplary embodiment of the present disclosure is schematically shown

[0049] Figure 3A schematic diagram of a user operation behavior feature value determination method in an exemplary embodiment of the present disclosure is shown schematically.

[0050] Figure 4 A flowchart of a fingerprint collision determination method in an exemplary embodiment of the present disclosure is shown schematically. Figure 2 ;

[0051] Figure 5 A block diagram of a fingerprint collision determination apparatus in an exemplary embodiment of the present disclosure is shown schematically.

[0052] Figure 6 A schematic diagram of a storage medium in an exemplary embodiment of the present disclosure is shown schematically.

[0053] Figure 7 A block diagram of an electronic device in an exemplary embodiment of the present disclosure is shown schematically.

[0054] In the drawings, the same or corresponding reference numbers denote the same or corresponding parts. DETAILED DESCRIPTION

[0055] The principles and spirits of the present application will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are given only to enable those skilled in the art to better understand and implement the present application, and do not limit the scope of the present application in any way. On the contrary, these embodiments are provided to make the present disclosure more thorough and complete, and to fully convey the scope of the present disclosure to those skilled in the art.

[0056] Those skilled in the art know that the embodiments of the present application can be implemented as a system, apparatus, device, method or computer program product. Therefore, the present disclosure can be embodied in the form of a complete hardware, complete software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0057] In view of the defects in the prior art, the present example embodiment first provides a fingerprint collision determination method, which can automatically detect whether a device has occurred fingerprint collision in time, avoids the lag problem existing in the prior art, is conducive to improving the correctness of business data, and greatly improves the user experience. Referring to Figure 1 The fingerprint collision determination method includes the following steps:

[0058] S11, obtaining the identification information of a target recovered device, and determining the fingerprint collision feature value of the target recovered device according to the identification information, the target recovered device being any one of the recovered devices;

[0059] S12, obtaining the sensor parameters collected by the sensor of the target recovered device;

[0060] S13, determining a user operation behavior feature value of the target recovered device according to the sensor parameter;

[0061] S14, determining whether the target recovered device has a fingerprint collision according to the fingerprint collision feature value and the user operation behavior feature value.

[0062] In an example embodiment of the present disclosure, the acquired identification information of the target recovered device can be input into a fingerprint collision function to acquire a fingerprint collision feature value of the target recovered device; the acquired sensor parameter of the target recovered device can be input into a user operation behavior function to acquire a user operation behavior feature value of the target recovered device. The fingerprint collision function is determined according to the identification information of the target recovered device, and the user operation behavior function is determined according to the sensor parameter of the target recovered device.

[0063] The method provided by the present disclosure can acquire the identification information of the target recovered device, determine a fingerprint collision feature value of the target recovered device according to the identification information, acquire a sensor parameter of the target recovered device, determine a user operation behavior feature value of the target recovered device according to the sensor parameter, and determine whether the target recovered device has a fingerprint collision according to the fingerprint collision feature value and the user operation behavior feature value. This scheme can automatically detect whether the device has a fingerprint collision, avoids the lag problem caused by the user query mode, is conducive to improving the correctness of the business data, and greatly improves the user experience.

[0064] In the following, each step in the fingerprint collision determination method in the example embodiment will be described in more detail with reference to the accompanying drawings and examples.

[0065] In step S11, the identification information of the target recovered device is acquired, and a fingerprint collision feature value of the target recovered device is determined according to the identification information.

[0066] In the example embodiment, the fingerprint collision function is determined according to the identification information of the target recovered device, and the user operation behavior function is determined according to the sensor parameter of the target recovered device. Figure 2The system architecture shown can include a user-side mobile terminal device 201, a user-side intelligent terminal device 204, a server 203, and the like. The user-side mobile terminal device 201, the user-side intelligent terminal device 204, and the server 203 can all transmit data through the network 202. The network can include various connection types, such as wired communication links, wireless communication links, and the like. The fingerprint collision determination method provided in the embodiments of the present application can be executed on the server side or by the user-side terminal device in cooperation with the server side. Taking the execution of the method on the server side as an example, the user uploads a fingerprint collision determination request to the server side on the terminal device side; the fingerprint collision determination request can contain identification information of the target recovered device. The server side can determine the fingerprint collision feature value of the target recovered device according to the identification information; obtain the sensor parameters collected by the sensor of the target recovered device; determine the user operation behavior feature value of the recovered device according to the sensor parameters; and determine whether the target recovered device has occurred fingerprint collision according to the fingerprint collision feature value and the user operation behavior feature value.

[0067] In an exemplary embodiment of the present disclosure, the identification information includes a device ID of the target recovered device, preset field information, and the like. The device ID of the target recovered device is the identification code of the target recovered device. For example, when the target recovered device is a mobile phone, the device ID of the target recovered device is the International Mobile Equipment Identity (IMEI) of the mobile phone. When the target recovered device is a computer, the device ID of the target recovered device is the device ID of the computer. The preset field information includes a pre-set stability field and / or a uniqueness field of the device. The uniqueness field of the device is, for example, a MAC (Media Access Control Address) address, and the stability field of the device is, for example, a brand field and an operating system field.

[0068] In an exemplary embodiment of the present disclosure, the determination of the fingerprint collision feature value of the target recovered device according to the identification information includes:

[0069] If the device ID of the target recovered device is consistent with that of another recovered device, and the preset field information of the target recovered device is inconsistent with that of the other recovered device, the fingerprint collision feature value of the target recovered device is determined to be a first preset threshold value.

[0070] In an exemplary embodiment of the present disclosure, the determination of the fingerprint collision feature value of the target recovered device according to the identification information further includes:

[0071] If the device ID of the target restored device is consistent with the device ID of the other restored device and the preset field information of the target restored device is consistent with the preset field information of the other restored device, the fingerprint collision characteristic value of the target restored device is determined as a second preset threshold, which is smaller than the first preset threshold.

[0072] In an exemplary embodiment of the present disclosure, the other restored device with the same ID as the target restored device is determined from the restored devices according to the ID of the target restored device, and then the preset field information of the target restored device and the preset field information of the other restored device are input into a fingerprint collision function to determine the fingerprint collision characteristic value of the target restored device.

[0073] Exemplarily, the fingerprint collision function is as follows:

[0074]

[0075] wherein f(x1, x2) represents the fingerprint collision function, x1 represents the target restored device, x2 represents the other restored device, x1 core index represents the preset field information of the target restored device, x2 core index represents the preset field information of the other restored device, m is the first preset threshold, and n is the second preset threshold. As shown in formula (1), when the preset field information of the target restored device is inconsistent with the preset field information of the other restored device, the target restored device and the other restored device are likely to send fingerprint collisions, and the value of f(x1, x2) is m, i.e. the fingerprint collision characteristic value of the target restored device is m. In an exemplary embodiment of the present disclosure, the value of m is 1. When the preset field information of the target restored device is consistent with the preset field information of the other restored device, the target restored device and the other restored device are the same device, and the value of f(x1, x2) is n, i.e. the fingerprint collision characteristic value of the target restored device is n. In an exemplary embodiment of the present disclosure, the value of n is 0.

[0076] In step S12, the sensor parameter collected by the sensor of the target restored device is acquired.

[0077] In an exemplary embodiment of the present disclosure, an acceleration sensor, a gyroscope, a gravity acceleration sensor, a magnetic field sensor and the like can be arranged on the target recovered device. The acceleration sensor can capture the motion mode of the target recovered device, such as shaking, swinging, turning and the like. Specifically, the acceleration sensor can measure the acceleration of the target recovered device in the X-axis, Y-axis and Z-axis directions. For example, the acceleration in the X-axis direction represents the movement of the phone in the horizontal direction, the acceleration in the Y-axis direction represents the movement of the phone in the vertical direction, and the acceleration in the Z-axis direction represents the movement of the phone in the space vertical direction. In addition, the value of the acceleration can be determined by the magnetic field sensor, and the direction pointing to the sky is positive, and the direction pointing to the earth is negative.

[0078] In an exemplary embodiment of the present disclosure, the gyroscope can collect the rotation rate acceleration of the target recovered device. Similarly, the rotation rate sensor includes the X-axis, Y-axis and Z-axis directions, and the value of the rotation rate acceleration is determined by the magnetic field sensor. The gravity acceleration sensor can collect the pressing force and the pressing area of the target recovered device by the user.

[0079] In step S13, the user operation behavior characteristic value of the target recovered device is determined according to the sensor parameters.

[0080] Based on the above, as shown in the present disclosure, in an exemplary embodiment of the present disclosure, the above-mentioned determination of the user operation behavior characteristic value of the target recovered device according to the sensor parameters includes: Figure 3

[0081] S131, a plurality of first sensor parameters collected by the sensor in a first preset time length are obtained;

[0082] S132, the plurality of first sensor parameters are screened to obtain screened sensor parameters;

[0083] S133, a first average value and a first variance value of the screened sensor parameters are obtained;

[0084] S134, the user operation behavior characteristic value of the target recovered device is determined according to the first average value and the first variance value.

[0085] ​Specifically, the plurality of first sensor parameters include X-axis direction acceleration Acc_x1, Y-axis direction acceleration Acc_y1 and Z-axis direction acceleration Acc_z1 collected by the acceleration sensor in a first preset time length, for example, in the past 15 days; X-axis direction rotation acceleration Gyro_x1, Y-axis direction rotation acceleration Gyro_y1 and Z-axis direction rotation acceleration Gyro_z1 collected by the gyroscope in the first preset time length, for example, in the past 15 days; and pressing force Press1 and pressing area Press_area1 of the user on the target recovered device collected by the gravity acceleration sensor in the first preset time length, for example, in the past 15 days.

[0086] Further, after obtaining the plurality of first sensor parameters, the plurality of first sensor parameters are screened to obtain screened sensor parameters. How to screen the plurality of first sensors to obtain screened acceleration is described below.

[0087] For example, the second sensor parameters collected by the sensor in a second preset time length are obtained, and the time sequence of the second preset time length is before the first preset time length; the second average value and the second variance value of the second sensor parameters are obtained; the screening interval is determined according to the second average value and the second variance value; the target sensor parameter located in the screening interval is determined from the first sensor parameters, and the target sensor parameter is taken as the screened sensor parameter.

[0088] Here, how to screen Acc_x1 is taken as an example for description. The second sensor parameter X-axis direction acceleration Acc_x2 of the acceleration sensor in a second preset time length before the first preset time length, for example, in the second preset time length of the test stage of the recovered device, is obtained, and the second average value Acc_x2_avg and the second variance value Acc_x2_std of Acc_x2 are obtained; further, the screening interval of Acc_x1 is determined according to Acc_x2_avg and the second variance value Acc_x2_std.

[0089] According to the normal distribution graph, the probability of the numerical value distribution in (μ-σ, μ+σ) is 0.6827, the probability of the numerical value distribution in (μ-2σ, μ+2σ) is 0.9545, and the probability of the numerical value distribution in (μ-3σ, μ+3σ) is 0.9973. It can be seen that the numerical value is almost concentrated in the interval (μ-3σ, μ+3σ), and the possibility of exceeding this range is only less than 0.3%. If the numerical value is not in the interval (μ-3σ, μ+3σ), it is basically determined as an abnormal data. Therefore, the screening interval of Acc_x1 can be determined according to the 3σ principle, and the specific screening interval is [Acc_x2_avg-3Acc_x2_std, Acc_x2_avg+3Acc_x2_std]. At the same time, the target sensor parameter located in [Acc_x2_avg-3Acc_x2_std, Acc_x2_avg+3Acc_x2_std] can be determined from Acc_x1, and the target sensor parameter is taken as the screened sensor parameter Acc_x, so as to effectively screen out abnormal data from Acc_x1. The screening process of other first sensor parameters such as Acc_y1 and Acc_z1 is similar to that of Acc_x1, and will not be described here.

[0090] Further, after obtaining the screened sensor parameters Acc_x, Acc_y, Acc_z, Gyro_x, Gyro_y, Gyro_z, Press and Press_area, Acc_x is taken as the first user operation behavior feature featuer_1, Acc_y is taken as the second user operation behavior feature featuer_2, Acc_z is taken as the third user operation behavior feature featuer_3, Gyro_x is taken as the fourth user operation behavior feature featuer_4, Gyro_y is taken as the fifth user operation behavior feature featuer_5, Gyro_z is taken as the sixth user operation behavior feature featuer_6, Press is taken as the seventh user operation behavior feature featuer_7, and Press_area is taken as the eighth user operation behavior feature featuer_8. The ninth user operation behavior feature featuer_9 is calculated according to formula (2):

[0091]

[0092] wherein,

[0093]

[0094] Further, after the user operation behavior features of each user are determined, the first average value and the first variance value of each user operation behavior feature are calculated, and the first average value and the first variance value of each user operation behavior feature are brought into the user operation behavior function as shown in formula (3) to calculate the user operation behavior feature value of the recovered device:

[0095]

[0096]

[0097] wherein g represents the user operation behavior function, feature i avg represents the first average value of the i th user operation behavior feature, feature i std represents the first variance value of the i th user operation behavior feature, and β is a constant. The value of β can be 0.5, or 0.6 or other values, which are not specifically limited herein.

[0098] In step S14, whether the recovered device has a fingerprint collision is determined according to the fingerprint collision feature value and the user operation behavior feature value.

[0099] Based on the above, as shown in formula (5), in an exemplary embodiment of the present disclosure, determining whether the recovered device has a fingerprint collision according to the fingerprint collision feature value and the user operation behavior feature value comprises: Figure 4

[0100] S141, obtaining a weighted sum value of the fingerprint collision feature value and the user operation behavior feature value;

[0101] S142, if the weighted sum value is greater than or equal to a third preset threshold value, determining that the target recovered device has a fingerprint collision;

[0102] S143, if the weighted sum value is less than the third preset threshold value, determining that the target recovered device does not have a fingerprint collision.

[0103] In an exemplary embodiment of the present disclosure, the weighted sum value of the fingerprint collision feature value and the user operation behavior feature value can be calculated according to formula (4):

[0104] F(x) = w1*f(x) + w2*g(x); (4)

[0105] wherein F(x) represents the corresponding weighted sum value of the recovered device x, f(x) represents the fingerprint collision feature value of the recovered device x, g(x) represents the user operation behavior feature value of the recovered device x, w1 represents the weight of f(x), and w2 represents the weight of g(x), and w2 is less than w1.

[0106] ​Further, if F(x) is greater than or equal to a third preset threshold, it is determined that the recovered device x has fingerprint collision; if F(x) is less than the third preset threshold, it is determined that the recovered device x does not have fingerprint collision.

[0107] Based on the above, in an example embodiment of the present disclosure, before the identification information of the target recovered device is acquired, the method further includes:

[0108] S15, acquiring a first device with a timestamp within a third preset time length;

[0109] S16, determining a second device recovered within a fourth preset time length from the first device, and taking the second device as the recovered device, the fourth preset time length being adjacent to the third preset time length and in time sequence after the third preset time length.

[0110] Since device fingerprint collision usually occurs after the device is recovered (for example, the device is reset or recovered to factory settings), the recovered device needs to be acquired first. In an example embodiment of the present disclosure, the timestamp of the first device can be the production date of the first device. For example, a first device with a production date within August 1, 2022 to August 31, 2022 can be acquired, and a second device recovered within 60 days after August 31, 2022 is determined from the first device, and the second device is taken as the recovered device. After the recovered device is acquired, whether the target recovered device has fingerprint collision is determined according to the steps of S11 to S14.

[0111] In summary, the method provided by the present disclosure can determine whether the target recovered device has fingerprint collision according to the fingerprint collision feature value and the user operation behavior feature value, can automatically detect whether the device has fingerprint collision, avoids the lag caused by the user query method, is beneficial to improve the correctness of business data, and greatly improves the user experience.

[0112] After introducing the method for determining fingerprint collision of the example embodiment of the present disclosure, next, with reference to Figure 5 The fingerprint collision determination device of the example embodiment of the present disclosure is described.

[0113] With reference to Figure 5 The fingerprint collision determination device 50 of the example embodiment of the present disclosure can include a fingerprint collision feature value determination module 501, a sensor parameter acquisition module 502, an operation behavior feature value acquisition module 503, and a fingerprint collision determination module 504, wherein:

[0114] The fingerprint collision characteristic value determination module 501 is configured to acquire identification information of a target recovered device, and determine a fingerprint collision characteristic value of the target recovered device according to the identification information, wherein the target recovered device is any one of the recovered devices.

[0115] The sensor parameter acquisition module 502 is configured to acquire a sensor parameter of the target recovered device.

[0116] The operation behavior characteristic value acquisition module 503 is configured to determine a user operation behavior characteristic value of the target recovered device according to the sensor parameter.

[0117] The fingerprint collision determination module 504 is configured to determine whether the target recovered device has a fingerprint collision according to the fingerprint collision characteristic value and the user operation behavior characteristic value.

[0118] In an example embodiment of the present disclosure, the identification information includes a device ID and preset field information of the target recovered device, and the fingerprint collision characteristic value determination module includes:

[0119] The fingerprint collision characteristic value determination unit is configured to determine the fingerprint collision characteristic value of the target recovered device as a first preset threshold value if the device ID of the target recovered device is consistent with that of other recovered devices and the preset field information of the target recovered device is inconsistent with that of the other recovered devices.

[0120] In an example embodiment of the present disclosure, the fingerprint collision characteristic value determination unit is further configured to:

[0121] If the device ID of the target recovered device is consistent with that of other recovered devices and the preset field information of the target recovered device is consistent with that of the other recovered devices, the fingerprint collision characteristic value of the target recovered device is determined as a second preset threshold value.

[0122] In an example embodiment of the present disclosure, the operation behavior characteristic value acquisition module includes:

[0123] The first sensor parameter acquisition unit is configured to acquire a plurality of first sensor parameters collected by the sensor within a first preset time length.

[0124] The sensor parameter screening unit is configured to screen the plurality of first sensor parameters to obtain screened sensor parameters.

[0125] The first average value and first variance value acquisition unit is configured to acquire a first average value and a first variance value of the screened sensor parameters.

[0126] The user operation behavior characteristic value determination unit is configured to determine a user operation behavior characteristic value of the target recovered device according to the first average value and the first variance value.

[0127] In an example embodiment of the present disclosure, the sensor parameter screening unit comprises:

[0128] The second sensor parameter acquisition unit is configured to acquire a second sensor parameter collected by the sensor within a second preset time length, the time sequence of the second preset time length being before the first preset time length.

[0129] The second average value and second variance value acquisition unit is configured to acquire a second average value and a second variance value of the second sensor parameter.

[0130] The screening interval determination unit is configured to determine a screening interval according to the second average value and the second variance value.

[0131] The target sensor parameter determination unit is configured to determine a target sensor parameter located in the screening interval from the first sensor parameter, and take the target sensor parameter as the screened sensor parameter.

[0132] In an example embodiment of the present disclosure, the fingerprint collision determination module comprises:

[0133] The weighted sum value acquisition unit is configured to acquire a weighted sum value of the fingerprint collision characteristic value and the user operation behavior characteristic value.

[0134] The fingerprint collision determination unit is configured to determine that the target recovered device has a fingerprint collision if the weighted sum value is greater than or equal to a third preset threshold value.

[0135] If the weighted sum value is less than the third preset threshold value, it is determined that the target recovered device does not have a fingerprint collision.

[0136] In an example embodiment of the present disclosure, the device further comprises:

[0137] The recovered device acquisition module is configured to:

[0138] acquire a first device within a third preset time length;

[0139] determine a second device recovered within a fourth preset time length from the first device, and take the second device as the recovered device, the fourth preset time length being adjacent to the third preset time length and having a time sequence after the third preset time length.

[0140] Since each functional module of the fingerprint collision determination apparatus of the embodiment of the present application is the same as the fingerprint collision determination method of the embodiment of the present application, the description is omitted here.

[0141] After introducing the fingerprint collision determination method and the fingerprint collision determination apparatus of the exemplary embodiment of the present application, next, with reference to Figure 6 The storage medium of the exemplary embodiment of the present application is described.

[0142] With reference to Figure 6 As shown in FIG. 6, a program product 600 for implementing the above method according to the embodiment of the present application is described, which can adopt a portable compact disc read-only memory (CD-ROM) and include program codes, and can run on a device, such as a personal computer. However, the program product of the present application is not limited to this, and in this document, the readable storage medium can be any tangible medium containing or storing a program, which can be used by or in conjunction with an instruction execution system, apparatus or device.

[0143] The program product can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or any combination of the above. More specific examples (non-exhaustive list) of the readable storage medium include an electrical connection having one or more wires, a portable disc, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0144] The computer readable signal medium can include a data signal propagated in a baseband or as a carrier wave in a propagated data signal, in which readable program codes are carried. Such a propagated data signal can adopt various forms, including but not limited to an electromagnetic signal, an optical signal or any suitable combination of the above. The readable signal medium can also be any readable medium other than the readable storage medium, which can send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device.

[0145] The program codes contained on the readable medium can be transmitted by any suitable medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the above.

[0146] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0147] After introducing the storage medium of exemplary embodiments of the present invention, the following references are made. Figure 7 An electronic device according to an exemplary embodiment of the present invention will be described.

[0148] Figure 7 The electronic device 70 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of the present invention.

[0149] like Figure 7 As shown, the electronic device 70 is presented in the form of a general-purpose computing device. The components of the electronic device 80 may include, but are not limited to: at least one processing unit 710, at least one storage unit 720, a bus 730 connecting different system components (including storage unit 720 and processing unit 710), and a display unit 740.

[0150] The storage unit stores program code that can be executed by the processing unit 710, causing the processing unit 710 to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of the present invention. For example, the processing unit 710 can perform actions such as... Figure 1 Steps S11 to S14 are shown in the diagram.

[0151] Storage unit 720 may include volatile storage units, such as random access memory (RAM) 7201 and / or cache memory 7202, and may further include read-only memory (ROM) 7203. Storage unit 720 may also include a program / utility 7204 having a set (at least one) of program modules 7205, such program modules 7205 including but not limited to: operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0152] Bus 730 may include a data bus, an address bus, and a control bus.

[0153] Electronic device 70 can also communicate with one or more external devices 80 (e.g., keyboard, pointing device, Bluetooth device, etc.) via input / output (I / O) interface 750. Electronic device 70 also includes a display unit 740 connected to input / output (I / O) interface 750 for display purposes. Furthermore, electronic device 80 can communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 760. As shown, network adapter 760 communicates with other modules of electronic device 70 via bus 730. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 70, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0154] It should be noted that although several modules or sub-modules of the rate control device have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of the present invention, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided and embodied by multiple units / modules.

[0155] Furthermore, although the operations of the method of the present invention are described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0156] While the spirit and principles of the invention have been described with reference to several specific embodiments, it should be understood that the invention is not limited to the disclosed specific embodiments, and the division of aspects does not imply that features in these aspects cannot be combined for benefit; such division is merely for ease of description. The invention is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.

Claims

1. A fingerprint collision determination method, characterized in that, include: Obtain the identification information of the target device to be restored, and determine the fingerprint collision feature value of the target device to be restored based on the identification information. The target device to be restored can be any one of the devices to be restored. The sensor parameters collected by the sensors of the target device to be restored are obtained; wherein, the sensor parameters include at least one of the motion mode of the target device to be restored, the acceleration and acceleration values ​​of the target device in the three directions of X-axis, Y-axis and Z-axis, the rotational acceleration and the rotational acceleration values ​​of the target device to be restored, and the pressure and pressure area of ​​the user on the target device to be restored. Determining the user operation behavior feature value of the target restored device based on the sensor parameters includes: acquiring multiple first sensor parameters collected by the sensor within a first preset time period; filtering the multiple first sensor parameters to obtain filtered sensor parameters; acquiring a first average value and a first variance value of the filtered sensor parameters; and determining the user operation behavior feature value of the target restored device based on the first average value and the first variance value. Whether the target device being restored has experienced a fingerprint collision is determined based on the fingerprint collision feature value and the user operation behavior feature value.

2. The method according to claim 1, characterized in that, The identification information includes the device ID of the target restored device and preset field information. Determining the fingerprint collision feature value of the target restored device based on the identification information includes: If the target device to be restored has the same device ID as other restored devices and the preset field information of the target device to be restored is inconsistent with that of the other restored devices, then the fingerprint collision feature value of the target device to be restored is determined to be a first preset threshold.

3. The method according to claim 1, characterized in that, The step of determining the fingerprint collision feature value of the target recovered device based on the identification information further includes: If the target recovered device has the same device ID as other recovered devices and the target recovered device has the same preset field information as other recovered devices, then the fingerprint collision feature value of the target recovered device is determined to be the second preset threshold.

4. The method according to claim 1, characterized in that, The step of filtering multiple first sensor parameters to obtain filtered sensor parameters includes: Acquire second sensor parameters collected by the sensor within a second preset time period, wherein the timing of the second preset time period precedes that of the first preset time period; Obtain the second average value and the second variance value of the second sensor parameters; The selection interval is determined based on the second average value and the second variance value; The target sensor parameter located in the filtering interval is determined from the first sensor parameter, and the target sensor parameter is used as the filtered sensor parameter.

5. The method according to claim 1, characterized in that, The step of determining whether a fingerprint collision has occurred in the target restored device based on the fingerprint collision feature value and the user operation behavior feature value includes: Obtain the weighted sum of the fingerprint collision feature value and the user operation behavior feature value; If the weighted sum is greater than or equal to the third preset threshold, it is determined that the target device being restored has experienced a fingerprint collision. If the weighted sum is less than the third preset threshold, it is determined that the target recovered device has not experienced a fingerprint collision.

6. The method according to any one of claims 1-5, characterized in that, Before obtaining the identification information of the recovered device, the method further includes: Obtain the first device whose timestamp is within the third preset duration; A second device is determined from the first device that is restored within a fourth preset time period, and the second device is taken as the restored device. The fourth preset time period is adjacent to the third preset time period and the timing is after the third preset time period.

7. A fingerprint collision determination device, characterized in that, include: The fingerprint collision feature value determination module is used to obtain the identification information of the target device to be restored, and determine the fingerprint collision feature value of the target device to be restored based on the identification information, wherein the target device to be restored is any one of the devices to be restored; The sensor parameter acquisition module is used to acquire sensor parameters collected by the sensors of the target device to be restored; wherein, the sensor parameters include at least one of the following: the motion mode of the target device to be restored, the acceleration and acceleration values ​​of the target device in the X-axis, Y-axis, and Z-axis directions, the rotational acceleration and the rotational acceleration values ​​of the target device to be restored, and the pressure and pressing area of ​​the user on the target device to be restored. An operation behavior feature value acquisition module is used to determine user operation behavior feature values ​​of the target restored device based on the sensor parameters, including: acquiring multiple first sensor parameters collected by the sensors within a first preset time period; filtering the multiple first sensor parameters to obtain filtered sensor parameters; acquiring a first average value and a first variance value of the filtered sensor parameters; and determining the user operation behavior feature values ​​of the target restored device based on the first average value and the first variance value. The fingerprint collision determination module is used to determine whether a fingerprint collision has occurred on the target restored device based on the fingerprint collision feature value and the user operation behavior feature value.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method according to any one of claims 1 to 6.

9. An electronic device, characterized in that, include: processor; as well as Memory for storing the executable instructions of the processor; The processor is configured to perform the steps of the method according to any one of claims 1 to 6 by executing the executable instructions.

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