Identity recognition method and device applied to electronic device, electronic device and storage medium

Abstract

The present specification provides an identity recognition method and device applied to an electronic device, an electronic device and a storage medium, and the method comprises: acquiring a network device fingerprint in a network detection environment. The identity of a target object is determined based on the network device fingerprint.

Description

TECHNICAL FIELD

[0001] The present specification relates to the technical field of information security, and particularly relates to an identity recognition method and device applied to an electronic device, an electronic device and a storage medium. BACKGROUND

[0002] Electronic devices used in daily scenarios are usually configured with multiple different running modes. For example, taking an electronic device such as a smart door lock as an example, typical running modes thereof include a regular running mode, a privacy protection running mode and a tracking monitoring running mode, etc. Taking an electronic device such as a mobile phone as an example, typical running modes thereof include a flight mode, etc.

[0003] Currently, the process of implementing running mode switching of an electronic device often needs to rely on a user to perform a manual setting operation. Specifically, in the application scenario of a smart door lock, a user needs to manually configure a trigger condition for the smart door lock to execute a privacy protection running mode; in the application scenario of a mobile phone, a user needs to manually operate to adjust the mobile phone to a flight mode, and the like all belong to a switching mode relying on a user to manually set. However, the above-mentioned running mode switching scheme relying on a user to manually set has a technical problem of a cumbersome operation process, thereby causing poor user experience in actual use.

[0004] A general technology of recognizing the identity of an object needs an electronic device to establish a communication connection with a target object to obtain related information of the target object, so as to determine the identity of the target object according to the related information. However, the way of establishing a communication connection with the target object has a risk of leaking private information of the electronic device. SUMMARY

[0005] To overcome the problems in the related art, the present specification provides an identity recognition method and device applied to an electronic device, an electronic device and a storage medium.

[0006] According to a first aspect of an embodiment of the present specification, an identity recognition method applied to an electronic device is provided, comprising:

[0007] obtaining a network device fingerprint in a network detection environment;

[0008] determining an identity of a target object based on the network device fingerprint.

[0009] According to a second aspect of an embodiment of the present specification, an identity recognition device applied to an electronic device is provided, comprising:

[0010] a network device fingerprint obtaining unit, configured to obtain a network device fingerprint in a network detection environment;

[0011] an object identity recognition unit, configured to determine an identity of a target object based on the network device fingerprint.

[0012] According to a third aspect of the embodiments of the present specification, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method according to the first aspect when executing the program.

[0013] According to a fourth aspect of the embodiments of the present specification, a computer readable storage medium is provided, which stores a computer program, wherein the program is executed by a processor to implement the steps of the method according to the first aspect.

[0014] According to a fifth aspect of the embodiments of the present specification, a computer program product is provided, comprising computer programs / instructions, which are executed by a processor to implement the steps of the method according to the first aspect.

[0015] The technical solutions provided by the embodiments of the present specification can include the following beneficial effects:

[0016] Since the network device fingerprint can be obtained from the network detection environment without establishing a communication connection with the target object, and then the identity of the target object is determined based on the network device fingerprint, on the one hand, the biological information of the specific target object can be avoided to be collected, or the collected biological information can be blurred, so as to avoid the private information of the specific target object to be collected, and thus the risk of private information leakage of the target object is avoided. On the other hand, the privacy of the network device can also be avoided to be leaked to the target object.

[0017] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present specification. BRIEF DESCRIPTION OF DRAWINGS

[0018] The accompanying drawings, which are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present specification, and together with the specification serve to explain the principles of the present specification.

[0019] Figure 1 is a flowchart of an identity recognition method applied to an electronic device according to an exemplary embodiment of the present specification.

[0020] Figure 2 is a schematic diagram of determining the identity of an object based on an index characteristic value according to an exemplary embodiment of the present specification.

[0021] Figure 3 is a structural schematic diagram of an electronic device according to an exemplary embodiment of the present specification.

[0022] Figure 4 is a block diagram of an identity recognition device applied to an electronic device according to an exemplary embodiment of the present specification. DETAILED DESCRIPTION

[0023] The exemplary embodiments will be described in detail herein with reference to the attached drawings. In the following description, like reference numerals refer to like elements throughout the description. The following exemplary embodiments are not representative of all embodiments consistent with the present description. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the present description as detailed in the appended claims.

[0024] The terminology used in the present description is for the purpose of describing particular embodiments only and is not intended to be limiting of the present description. As used in the present description and the appended claims, the singular forms "a," "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term "and / or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

[0025] It will be understood that, although the terms first, second, third, etc. can be used herein to describe various information, these terms are not intended to denote a temporal or chronological order. Rather, these terms are used only to distinguish different sets of information from one another. For example, a first information can be termed a second information, and similarly, a second information can also be termed a first information, without departing from the scope of the present description. As used herein, the word "if' can be construed to mean "when" or "in response to determining" depending on the context.

[0026] Electronic devices such as smart door locks, mobile phones, etc. usually have different operating modes, for example, a smart door lock has a regular operating mode, a privacy protection operating mode, a tracking monitoring operating mode, etc., and a mobile phone has a flight mode, etc.

[0027] At present, before performing automatic switching of operating modes, an electronic device needs to identify the identity of a target object, so as to automatically switch different operating modes for different object identities.

[0028] The general technology of identifying the identity of an object requires the electronic device to establish a communication connection with the target object to obtain relevant information of the target object, so as to determine the identity of the target object according to the relevant information. However, the way of establishing a communication connection with the target object has the risk of leaking private information of the electronic device.

[0029] Therefore, the present description provides an identity identification method applied to an electronic device, so as to switch operating modes and avoid the risk of leaking private information of the network device.

[0030] The implementation process of the present description will be introduced below taking an electronic device as an example, which is a security monitoring device such as a smart door lock, a smart doorbell, etc.

[0031] These security monitoring devices generally integrate functions such as facial recognition, wide-angle cameras, remote monitoring, and human body sensing. While these technologies improve security efficiency, they may also cover the entry and exit paths or indoor spaces of neighbors, visitors, etc., leading to the risk of privacy leaks.

[0032] Taking smart door locks as an example, smart door locks are usually equipped with cameras that capture images of the area around the door for security monitoring. During this process, the camera may capture facial images of neighbors, and may also capture images of their homes when they open their doors, leading to a risk of privacy breaches.

[0033] This method can be applied to electronic devices with biometric data acquisition modules, such as surveillance equipment and smart door locks. These biometric data acquisition modules can be facial information acquisition modules, biological behavioral information acquisition modules, biological physiological information acquisition modules, and so on.

[0034] It is understandable that electronic devices equipped with biometric data collection modules pose a risk of privacy breaches if the collected biometric data is illegally obtained. Therefore, this solution addresses this type of electronic device by identifying the identity types of objects in the surrounding environment, thus supporting the device in switching between different operating modes based on these identity types.

[0035] In different identity recognition scenarios, after an identity is identified, the operating mode that the electronic device switches to for the specific type of identity may not be the same, and it can freely choose according to the actual application scenario.

[0036] For example, a community might want to identify both community members and non-members, and upon identifying community members, implement specific privacy protection measures, such as disabling biometric data collection. Similarly, a business might want to identify employees and visitors, implementing special monitoring measures for visitors and specific privacy protection measures for employees. Furthermore, a household might want its cameras or smart locks to not monitor its own family members, and therefore automatically disable monitoring when a family member is detected approaching. Likewise, they might not want their cameras or smart locks to monitor neighbors, and could automatically disable monitoring when a neighbor is detected approaching.

[0037] It should be noted that after the identity of the target object is identified, the electronic device can automatically switch the running mode corresponding to the identified identity according to the identity of the object. The present scheme does not limit the content and manner of switching the running mode of the electronic device after the target object is identified.

[0038] Figure 1 is a flowchart of an identity recognition method applied to an electronic device according to an exemplary embodiment. As shown in Figure 1 , the method comprises steps 101-102:

[0039] Step 101: Obtain network device fingerprints in a network detection environment.

[0040] Step 102: Determine the identity of a target object based on the network device fingerprints.

[0041] In the present embodiment, the network device fingerprints can be obtained from the network detection environment without establishing a communication connection with the target object, and then the identity of the target object is determined based on the network device fingerprints. On the one hand, the biological information of a specific target object can be avoided to be collected, or the biological information collected is blurred, so as to avoid the private information of the specific target object to be collected, and thus the risk of private information leakage of the target object is avoided. On the other hand, the private information of the network device can also be avoided to be leaked to the target object.

[0042] In an embodiment, the network detection environment can be understood as an effective signal range in which the electronic device can actually detect the objects appearing around. The network device fingerprint can be understood as a signal detected by the electronic device in the network detection environment where the electronic device is located. The signal can be used to identify the identity of the network device. The signal can be actively sent by the network device and intercepted by the electronic device, or the signal can be actively sent by the electronic device and returned after the target object is detected.

[0043] In an embodiment, the target object can be a biological object, which can be exemplarily a person holding a network device, or the network device itself.

[0044] If the target object is a person holding a network device, the identity of the network device corresponds to the identity of the person. After the identity of the device is identified based on the network device fingerprint, the identity of the person can be determined according to the identity of the device. For example, in an exemplary monitoring scene at the entrance of a household, it is assumed that the identity of the network device is identified based on the network device fingerprint, and the network device is connected with the electronic device of the household. It can be judged that the device is the device of the household itself, and it can be judged that the person holding the device is the household itself.

[0045] If the target object is the device itself, the network device identity can be determined directly. For example, taking a door lock as a neighbor of the network device as an example, and also in the example of the monitoring scene at the entrance of the home, for example, the electronic device detects that the signal strength of the Bluetooth signal of a network device existing in the detection environment is stable in a fixed interval, and the duration is greater than a preset threshold, then the network device can be determined as the neighbor's door lock according to these information, and before the neighbor appears in the monitoring range, the neighbor's door lock will automatically connect to the neighbor's portable device, or send a sound signal in advance. According to the change of the connection state of the neighbor's door lock or the sound signal, for example, the connection state of the neighbor's door lock is switched from a continuous disconnected state to a connected state, or the door lock sends a sound such as "welcome home" which is captured by the electronic device, the neighbor will appear in the monitoring range can be further determined according to these information, and the running mode of the electronic device is switched to the mode corresponding to the neighbor's identity.

[0046] In an embodiment, if the identified target object is a living being, the target object identity can be determined based on the network device fingerprint only, or based on the biological behavior information only, or based on the network device fingerprint and the biological behavior information.

[0047] For example, for the scenario where the network device is separated from the person, for example, the neighbor's door lock is not attached to the neighbor, the network device identity can be determined based on the network device fingerprint only. Of course, the identity of the living being can also be determined by directly detecting the biological behavior information of the living being about to appear in the network detection environment. Or when the living being and the network device appear together, for example, the person holding the network device, the target object identity can be determined by combining the network device fingerprint and the biological behavior information.

[0048] The biological behavior information can include moving speed, moving direction, step length, step frequency, moving range, and stay duration in different space regions.

[0049] In an example monitoring scene, for example, a monitoring scene at the entrance of the home, if it is not desired that the monitoring device at the entrance of the home collects the biological information of the neighbor, the biological information collection function can be turned off when the neighbor appears in the monitoring range, or the collected biological information of the neighbor can be encrypted, processed by mosaic, etc.

[0050] In an example monitoring scene at the entrance of the door, after the smart door lock detects that a person appears in the detection range, if it is identified that the detected person is a dangerous person, for example, a suspicious person, a wanted person, etc., the alarm mode can be started to remind the target object that he has entered the monitoring range. For example, a loudspeaker can be controlled to broadcast a prompt information "you have entered the monitoring range". In addition, the smart door lock can also push a prompt to the family members that someone has entered the monitoring range, for example, a prompt information "a suspicious person appears" can be pushed to the owner's mobile phone, etc.

[0051] It is worth noting that the above operating modes are not all mutually exclusive operating modes, and in some scenarios, multiple operating modes can be executed simultaneously, for example, when the intelligent door lock detects a dangerous person, it can switch to the tracking monitoring operating mode while starting the reminder mode.

[0052] In the prior art, in order to meet the demand for privacy protection, the ID of the network device that needs privacy protection is stored in the whitelist. Generally, the network device and the monitoring device are in the same local area network, and the monitoring device can obtain the ID of the network device through the connection mode of the local area network, so as to switch to the privacy protection mode when it is determined that the ID of the network device appears in the whitelist.

[0053] However, the above-mentioned method can only be used for internal network devices and cannot identify non-internal network devices that do not establish a connection with the monitoring device. Of course, due to network security reasons, the monitoring device or the local area network where the monitoring device is located does not want to establish any connection with untrusted devices.

[0054] It is not difficult to understand that the existing scheme will fail when identifying the identity of non-internal network devices. For example, the network device of the existing scheme needs to establish a connection with the monitoring device to start object recognition, and due to network security considerations, neighbor devices generally cannot establish a connection with the monitoring device of other families, so the existing technical solution cannot be applied to identify the identity information of neighbors.

[0055] In an embodiment, the network device fingerprint in the network detection environment can be obtained in a non-established state, and the non-connected state can be that the electronic device has not established any effective communication link with the network device.

[0056] In this embodiment, the network device fingerprint is obtained in a non-connected manner, which can avoid the risk of data leakage of the electronic device itself, and can determine the identity of the target object based on the obtained network device fingerprint without the authorization of others.

[0057] In an embodiment, the non-connected manner can be to detect micro-motion features (such as breathing rate, limb movement) in the network detection environment through a radar module.

[0058] The non-connected mode can also be achieved by the BLE (Bluetooth Low Energy) sniffing function of the Bluetooth device, which is used to capture the data packets transmitted by the network equipment appearing in the network detection environment through the BLE technology. For example, the device identification (Hashed MAC) in the captured broadcast packet, signal strength (RSSI, Received Signal Strength Indicator), service UUID (Universally Unique Identifier) (such as 0xFDEE private service of Apple), and transmission power. Random MAC tracking technology is adopted: the broadcast packet length, carrier frequency offset (CFO, Carrier Frequency Offset) of the same device are used to establish the radio frequency fingerprint (such as the CFO of the Apple device: 5.2 kHz ± 0.1 kHz).

[0059] The non-connected mode can be achieved by the deep analysis function of the Wi-Fi system to analyze the Wi-Fi signals appearing nearby. For example, the SSID (Service Set Identifier) (such as “Starbucks”) is extracted from the Probe Request frame, the Vendor OUI (Organizationally Unique Identifier) (such as Apple 00:17:F2), the supported rate (such as 802.11ax for mobile phones), and the like.

[0060] The non-connected mode can also be achieved by the UWB (Ultra-Wideband) positioning function to measure the distance of the network equipment and thus infer the location of the network equipment.

[0061] Through the above-mentioned manner, the network equipment fingerprint can be obtained based on the non-connected mode without establishing any effective communication link between the electronic device and the network equipment. It should be noted that one or more network equipment fingerprints can be obtained based on one or more of the above-mentioned manners.

[0062] In an embodiment, when acquiring the network device fingerprints in the network detection environment, the network device fingerprints in the network detection environment can be acquired based on multiple non-established states. In the case of acquiring the network device fingerprints in the network detection environment based on multiple non-established states, the first network device fingerprints in the network detection environment can be acquired by the low-power detection device in a non-connected state, and whether the high-precision detection device needs to be activated can be determined based on the first network device fingerprints. In the case of needing, the high-precision detection device can be activated, and the second network device fingerprints acquired by the high-precision detection device can be acquired. The identity of the target object can be determined based on the first network device fingerprints and the second network device fingerprints. The low-power detection device can include a radar and a Bluetooth device. The high-precision detection device can include a UWB positioning device and a WI-FI system.

[0063] For example, if the behavior information of the object is determined based on the first network device fingerprints, and the behavior information indicates that the object will appear, it is determined that the high-precision detection device needs to be activated. For example, in an exemplary home doorway monitoring scene, a person holding a network device who takes an elevator or other people holding a network device who appear near the corridor but do not walk towards the monitoring range are detected by the low-power detection device. When it is determined based on the acquired network device fingerprints that the person will not appear in the monitoring range, the high-precision detection device can not be activated. For example, assuming that the low-power detection device is Bluetooth, the judgment basis can be that the Bluetooth signal gradually strengthens, it can be determined that someone is approaching nearby, and the high-precision detection device can be activated to further identify the identity of the person. If the signal slightly strengthens and then rapidly weakens, it can be determined that the person is passing by near the monitoring range, and the high-precision detection device can not be activated to further identify the identity of the person.

[0064] In the embodiment, the object appearing in the network detection environment is detected by the low-power detection device, and the high-precision detection device is started only when it is determined that the object needs to be identified to acquire the network device fingerprints in the network detection environment together with the low-power detection device. On the one hand, the energy consumption of the high-precision detection device can be saved, and the high-precision detection device can be avoided from being started unnecessarily. On the other hand, the identity of the target object can be identified based on multiple detection results of multiple detection devices, and the accuracy of identification can be improved.

[0065] In an embodiment, when the device identity is identified based on the network device fingerprints, at least one feature index value of at least one dimension of device features can be extracted from the network device fingerprints, and the device identity of the corresponding network device can be determined based on the at least one feature index value.

[0066] In an embodiment, the dimensions of the device features can be divided into a physical dimension, a protocol dimension, and a behavior dimension, wherein: the feature indicators of the physical features of the physical dimension include, but are not limited to, carrier frequency offset, modulation error, and signal strength fluctuation standard deviation; the feature indicators of the protocol features of the protocol dimension include, but are not limited to, BLE service and Wi-Fi probe; and the feature indicators of the behavior features of the behavior dimension include, but are not limited to, time period activity, stay duration, and movement trajectory.

[0067] Wherein, the extraction manner of the carrier frequency offset, the modulation error, and the signal strength fluctuation standard deviation is not subject to any limitation, the BLE service exemplarily can extract UUID, and the Wi-Fi probe exemplarily can extract WiFi channel width or OUI.

[0068] And the time period activity, the stay duration, and the movement trajectory can be indicators calculated according to the detection results, and are used for analyzing the behavior information of the target object.

[0069] Exemplarily, the time period activity can calculate the active interval of the target object within 24 hours through -∑(p*log2(p)), so as to capture the regularity of the appearance time of the target object. Wherein, P can be the probability of appearing in a specific time interval. Wherein, the smaller the time period activity is, the more fixed the time period of the appearance of the target object is.

[0070] Exemplarily, the stay duration coefficient of variation can be represented by the ratio of the standard deviation and the average value of the stay duration of the same network device within a week. This indicator can be used to analyze the regularity of the stay duration of the target object.

[0071] Exemplarily, the movement trajectory smoothness can be measured by the ratio of the straight line distance and the actual path length of the target object. The closer the ratio is to 1, the closer the movement path of the target object is to a straight line, and the closer the ratio is to 0, the greater the degree of detour of the movement path of the target object is.

[0072] In an embodiment, when the device identity of the corresponding network device is determined according to the feature indicator value, at least one feature indicator value can be fused to obtain a first fused indicator value of the corresponding dimension, and the device identity of the corresponding network device is determined according to the first fused indicator value of the corresponding dimension.

[0073] Exemplarily, if the device features are divided into one dimension, and there is only one feature indicator value under this dimension, the device identity of the corresponding network device can be determined based on the feature indicator value.

[0074] Exemplarily, if the device features are divided into one dimension, and there are multiple feature indicator values under the dimension, the multiple feature indicator values can be fused to obtain a first fused indicator value, and the device identity of the corresponding network device is determined according to the first fused indicator value.

[0075] Exemplarily, if the device features are divided into multiple dimensions, and there is at least one feature indicator value under each dimension, at least part of the feature indicator values of at least part of the dimensions can be selected for fusion processing to obtain a first fused indicator value of the corresponding dimension, and the device identity of the corresponding network device is determined according to the first fused indicator value of the corresponding dimension.

[0076] For example, for dimension A, a1, a2 and a3 are included under the dimension; for dimension B, b1 and b2 are included under the dimension; and for dimension C, c1, c2 and c3 are included under the dimension. The first fused indicator value under dimension A can be determined according to a1 and a2; the first fused indicator value under dimension B can be determined according to b1 and b2; the first fused indicator value under dimension C can be determined according to c1, c2 and c3, and the device identity of the corresponding network device is determined according to the first fused indicator values of the respective dimensions.

[0077] In an embodiment, when the device identity of the corresponding network device is determined according to the first fused indicator value of the corresponding dimension, the current environment scenario category can be determined, the first weight of each first fused indicator value corresponding to the current environment scenario category can be obtained, or the first weight of each first fused indicator value can be adaptively determined. The first fused indicator values are fused according to the first weight to obtain a second fused indicator value, and the device identity of the corresponding network device is determined according to the second fused indicator value.

[0078] For example, the environment scenario can be divided into a night mode, a strong interference environment mode and a long-term monitoring mode. For the night mode, the physical layer weight is greater than the behavior layer weight, which is greater than the protocol layer weight; for the strong interference environment mode, the physical layer weight is greater than or equal to the protocol layer weight, which is greater than the behavior layer weight; and for the long-term monitoring mode, the physical layer weight is less than the protocol layer weight, which is less than the behavior layer weight. The environment scenario category can be set by the user on the terminal, and the electronic device can fuse the first fused indicator values of the respective dimensions according to the weight corresponding to the environment scenario category set by the user to obtain a second fused indicator value.

[0079] In an embodiment, when weighting is performed, confidence can also be used for weighting, as shown in the following formula:

[0080]

[0081] wherein, is the second fused indicator value, Let be the weight of the k-th layer dimension in the fusion process. The confidence level of the first fusion index value in the k-th dimension. is the first fusion index value of the k-th dimension.

[0082] In one embodiment, when determining the device identity of a corresponding network device based on feature index values, an identification result can be generated based on the feature index values, and the device identity of the corresponding network device can be determined based on the identification result.

[0083] The identification result can be represented by any feature index value. For example, the magnitude of the feature index value of any dimension of the device feature can be used as the identification result.

[0084] The recognition result can also be represented by a first fusion index value or a second fusion index value. For example, multiple feature index values ​​in any dimension are fused to obtain a first fusion index value, and the magnitude of this first fusion index value can be used as the recognition result. Alternatively, first fusion index values ​​from different dimensions are fused to obtain a second fusion index value, and the magnitude of this second fusion index value can be used as the recognition result.

[0085] For example, in a typical home security monitoring scenario, the identification result can be normalized to [0, 1]. Assuming [0, 0.3] corresponds to the resident's identity, (0.3, 0.6] corresponds to the neighbor's identity, and (0.6, 1] corresponds to other people's identities, the identity of the target object can be determined based on the interval where the identification result falls. For example, if the identification result is 0.45, the target object's identity can be determined to be a neighbor. The range of this value is closely related to the application scenario, and those skilled in the art can reset the identity type corresponding to different numerical intervals of the identification result according to actual needs.

[0086] Specifically, such as Figure 2 As shown, assuming at least one feature value from the physical dimension, protocol dimension, and behavioral dimension is extracted from the network device fingerprint, any one of these feature values ​​can be used alone as an identification result. For example, if the physical dimension feature value 1 is 0.45, then the target object can be identified as a neighbor.

[0087] The plurality of characteristic indicator values in any dimension can be fused to obtain a first fused indicator value. Any fused indicator value can be used as a recognition result for identity recognition. For example, if the first fused indicator value corresponding to the physical dimension is 0.45, the identity of the target object can be determined as a neighbor. Or, if the first fused indicator value corresponding to the protocol dimension is 0.45, the identity of the target object can be determined as a neighbor. When performing fusion processing, the plurality of characteristic indicator values can be weighted, and higher weights are given to characteristic indicator values with higher importance, and lower weights are given to characteristic indicator values with lower importance.

[0088] The plurality of first fused indicator values can be fused to obtain a second fused indicator value. The second fused indicator value can be used as a recognition result for identity recognition. For example, if the second fused indicator value is 0.45, the identity of the target object can be determined as a neighbor. When performing fusion processing, the plurality of first fused indicator values can be weighted, and higher weights are given to first fused indicator values with higher importance, and lower weights are given to first fused indicator values with lower importance.

[0089] In an embodiment, for the recognition results determined at adjacent time instants in a continuous time period, if the difference between the recognition result at the current time instant and the recognition result at the previous time instant is greater than a preset threshold, the recognition result at the current time instant is adjusted according to a time decay strategy based on the recognition result at the previous time instant. The identity of the target object at the current time instant is determined according to the adjusted recognition result.

[0090] The recognition result at the current time instant can be adjusted based on the recognition result at the previous time instant by the following formula:

[0091]

[0092] wherein, is the recognition result at time instant t, is the recognition result at time instant t-1, is the difference between t and t-1.

[0093] For example, if network device fingerprints are obtained based on multiple detection devices, and the recognition results of different devices conflict, time decay arbitration can be introduced to prevent errors caused by conflicts.

[0094] For example, as shown in Table 1:

[0095] Table 1

[0096]

[0097] ​​​​In Table 1, at the moment t=0, the decision result based on BLE, at this moment, the decision state is to be confirmed, and the target object identity is not directly determined according to the current device fingerprint; at the moment t=2, continuous monitoring can be performed, and the decision based on UWB, as time elapses, the weight of the decision result of BLE at the previous moment in the UWB decision is reduced, and through continuous adjustment, conflicts between each other can be prevented. At the moment t=5, the response is terminated, and the monitoring is stopped.

[0098] In an embodiment, after obtaining the network device fingerprint in the network detection environment, the current connection state between the network device and the electronic device can also be determined based on the network device fingerprint. When the device identity of the corresponding network device is determined based on the network device fingerprint, in response to the current connection state being connected, the device identity of the network device is determined as a first type of device; and in response to the current connection state being unconnected, the device identity of the network device is determined as a second type of device.

[0099] In an exemplary home door monitoring scene, the first type of device is the device of the resident, and the second type of device is the device of other people.

[0100] In this embodiment, it is assumed that only the resident can establish a connection with the electronic device, and based on the current connection state between the network device and the electronic device, the device that establishes a connection with the electronic device can be accurately identified as the device of the resident.

[0101] In an embodiment, in response to the current connection state being unconnected, the historical activity of the network device can also be determined based on the network device fingerprint, and in the case where the historical activity is greater than a preset threshold, the network device is determined as a third type of device, wherein the third type of device is the device of the neighbor.

[0102] In combination with the above two embodiments, the device of the resident and the device of other people are distinguished by the connection state, and the neighbor device can be further identified from the device of other people by the historical activity. In this way, without establishing a connection with any network device, the device of the resident and the device of the neighbor can also be identified.

[0103] The historical activity can be measured by the period activity, the stay duration coefficient of variation and other characteristic indexes in the foregoing embodiments. If the period activity is used for measurement, the historical activity is the reciprocal of the period activity. For example, if the period activity is less than 1.5, it can be determined as the device of the neighbor. If the stay duration coefficient of variation is used for measurement, the historical activity can be the reciprocal of the stay duration coefficient of variation. For example, if the stay duration coefficient of variation is less than 0.1, it can be determined as the device of the neighbor.

[0104] Of course, the present solution is not limited to determining the identity of other objects based on the network device fingerprint. For example, the identity of a service personnel can also be determined based on the UUID in the network device fingerprint.

[0105] In an embodiment, network device fingerprints appearing in the network probing environment within a preset time period can be acquired, the device identity of the corresponding network device can be determined based on the network device fingerprint, a management list having a mapping relationship with the network device identity can be created based on the device identity, and the network device can be added to the corresponding management list.

[0106] Illustratively, the network device fingerprints appearing in the network probing environment within a preset time period can be network device fingerprints in the network probing environment acquired in real time at present, or network device fingerprints of the same network device within a historical time period.

[0107] In an embodiment, the network device fingerprint in the network probing environment is acquired, if the network device corresponding to the network device fingerprint is in any management list, the identity of the target object is determined as the identity corresponding to the any management list. If the network device corresponding to the network device fingerprint is not in any management list, the identity of the target object is determined based on the network device fingerprint.

[0108] In the present embodiment, different management lists correspond to the identities of different objects, after the device identity of the network device is determined, the network device can be added to the corresponding management list. When the network device in the management list appears again next time, the identity of the target object can be determined based on the management list, without the need to determine the identity of the target object based on the network device fingerprint again. On the one hand, the efficiency of determining the identity of the repeatedly appearing target object can be improved, and on the other hand, the error of determining the identity of the target object based on the network device fingerprint can be reduced.

[0109] In an embodiment, when the network device is added to the corresponding management list, the network device fingerprint representing the identity of the network device satisfying a first preset condition can be added to a first list; the network device fingerprint representing the identity of the network device satisfying a second preset condition can be added to a second list.

[0110] Illustratively, the network device fingerprint can uniquely identify the network device, and whether the network device appears in the management list can be determined by matching the network device fingerprint acquired in the network probing environment with the network device fingerprint stored in the management list.

[0111] Illustratively, the first list can be a permanent white list for storing the network device fingerprint of the network device of the household, and the second list can be a neighbor list for storing the network device fingerprint of the network device of the neighbor.

[0112] The first preset condition can be that the network device establishes a connection with the electronic device, or that the network device establishes a connection with the electronic device and the trajectory smoothness is greater than a preset value, or that the network device establishes a connection with the electronic device and an opening door action is detected within a preset time length. For example, the network device establishes a connection with the electronic device, which indicates that the network device is a device of the resident. For another example, it is detected that the network device establishes a connection with the electronic device and the trajectory smoothness is greater than a preset threshold value, which indicates that the network device is in a straight line and is close to the electronic device. For another example, it is detected that the network device establishes a connection with the electronic device and an opening door action is detected within a preset time, which can double-verify that the network device establishing a connection is a device of the resident.

[0113] According to the network device fingerprint, a heat map corresponding to the network device is generated, and the heat map is used to represent the time sequence aggregation feature and / or the area aggregation feature of the appearance of the corresponding network device. The second preset condition can be that the network device does not establish a connection with the electronic device and appears n times in a fixed time period. The fixed time period can be measured in the form of a heat map, and whether the network device appears n times in the fixed time period and the fixed area can be determined by measuring the similarity of the heat maps of the n appearances. The time stamp of the appearance of the network device can also be recorded, and only whether the network device appears n times in the fixed time period can be determined.

[0114] In an embodiment, the user can also manually add the network device to the management list. In response to a network device adding instruction containing a network device fingerprint, the network device is added to the corresponding management list.

[0115] The user can know the identity of the device, for example, the user can add the device of a family member to the corresponding management list.

[0116] The user does not know the identity of the device, for example, the user can search for a non-family member Bluetooth signal appearing nearby, and input the network device fingerprint of the network device sending the Bluetooth signal to the electronic device, so as to determine the corresponding management list and add the network device to the corresponding management list.

[0117] In an embodiment, the management list can also include an observation list. When the network device is added to the corresponding management list, the network device fingerprint representing the identity of the network device that meets the third preset condition can be added to the observation list.

[0118] Exemplarily, the observation list can be a service personnel list, such as a delivery person. The third preset condition can be that the network device fingerprint contains a UUID, and the UUID is a UUID exclusive to the service personnel. Of course, the observation list can also be a list of other personnel, and the third preset condition can be adjusted to correspond to the identity recognition of the other personnel.

[0119] In an embodiment, for each network device in the observation list, the network device is removed from the management list if the network device meets a fourth preset condition.

[0120] Exemplarily, the fourth preset condition can be that the network device does not appear again within a preset time period in the future.

[0121] In an embodiment, the management list further includes a temporary list, when a network device is added to a corresponding management list, the network device can be added to the temporary list, and the appearance frequency of the network device is updated, and if the appearance frequency is greater than a frequency threshold, the network device is added to the management list corresponding to the personnel identity.

[0122] In this embodiment, by judging the frequency of appearance in the temporary list, the network device that appears accidentally or is misrecognized is prevented from being added to the management list, thereby improving the accuracy of the identity of the network device in the management list.

[0123] In an embodiment, different operation modes are executed based on different management lists.

[0124] For example, in an exemplary home doorway monitoring scene, the management list can include a neighbor list and a resident list. In actual application, the operation mode can include a normal mode and a privacy protection mode. If a network device in the resident list is detected at the doorway of the home, the privacy protection mode is executed for the electronic devices indoors and outdoors. If a network device in the neighbor list is detected at the doorway of the home, the privacy protection mode is executed for the electronic devices outdoors. In other cases, the operation mode can be the normal mode. The privacy protection mode can be closing the biological information acquisition module, adjusting the angle of the biological information acquisition module so as not to be aligned with the target object, reducing the resolution of the biological information acquisition module, and performing blur processing on the image collected by the biological information acquisition module.

[0125] The implementation process of the present specification will be introduced below taking the electronic device as a terminal device such as a mobile phone, a tablet computer, a notebook computer, and a wearable device.

[0126] The electronic device described above usually has a flight mode, also called an aviation mode or a fly mode, which can cut off all communication signals after being turned on, so as to avoid the emission and reception of device signals affecting the flight of an airplane.

[0127] Taking the electronic device as a mobile phone as an example, the technical solution provided by the present specification can acquire the network device fingerprints in the network detection range of the mobile phone, and then determine the identity of the target object based on the network device fingerprints. When the identity of the target object indicates that the target object is a network device deployed on an airplane, it indicates that the mobile phone is located on the airplane, and the user may be about to take the airplane for flight, and then the flight mode can be automatically switched.

[0128] In this embodiment, the mobile phone can acquire the network device fingerprint to the network device in the network detection range in a non-connected state, where the non-connected state means that no valid communication link is established between the electronic device and the network device in the detection range. For example, no Bluetooth communication link is established between the electronic device and the network device, no Wi-Fi communication link is established, etc. In this embodiment, the network device fingerprint is acquired in a non-connected manner, and the electronic device such as the mobile phone does not need to establish a valid connection with the network device during the whole process, which is more widely applicable, and can also effectively avoid the risk of information leakage of the electronic device caused by establishing a connection with the network device.

[0129] In this embodiment, the network device fingerprint of the network device deployed on the airplane usually carries a specific identifier for representing the identity thereof, for example, the airline identifier can be carried in the SSID. The mobile phone can identify that the identity of the target object is the network device on the airplane by analyzing and identifying the detected network device fingerprint.

[0130] As can be seen from the above description, by using the above scheme provided in the specification, the terminal device such as the mobile phone can acquire the network device fingerprint in the network detection range, and determine the identity of the target object appearing in the network detection range based on the network device fingerprint. When the target object is the network device on the airplane, the flight mode can be automatically switched, without the need for manual switching by the user, which greatly improves the user experience.

[0131] The network detection range and the network device fingerprint can refer to the specific description of the foregoing embodiments, which will not be described here.

[0132] Corresponding to the foregoing method embodiments, the specification also provides an embodiment of an apparatus and a terminal to which the apparatus is applied.

[0133] Figure 3 is a structural schematic diagram of an electronic device according to an exemplary embodiment. As shown in the figure, at the hardware level, the electronic device 300 includes a processor 302, an internal bus 304, a network interface 306, a memory 308, and a non-volatile memory 310, and of course, other hardware required by the business. One or more embodiments of the specification can be implemented in a software manner, such as reading a corresponding computer program from the non-volatile memory 310 into the memory 308 by the processor 302 and then running. Of course, in addition to the software implementation manner, one or more embodiments of the specification do not exclude other implementation manners, such as logic devices or a combination of software and hardware, etc., that is, the execution subject of the following processing flow is not limited to each logical module, but can also be hardware or a logic device. Figure 3

[0134] Figure 4 ​is a block diagram of an identity recognition device applied to an electronic device according to an example embodiment. As shown in Figure 4 the device can be applied to the electronic device 300 as shown in Figure 3 to implement the technical solutions of the present specification. The device comprises:

[0135] a network device fingerprint acquisition unit 402 configured to acquire network device fingerprints in a network detection environment.

[0136] an object identity recognition unit 404 configured to determine the identity of a target object based on the network device fingerprints.

[0137] Optionally, the network device fingerprints in the network detection environment are acquired in a non-established state; and the non-connected state is that the electronic device has not established any valid communication link with the network device.

[0138] Optionally, the object identity recognition unit 404 is specifically configured to identify the device identity based on the network device fingerprints and determine the identity of the target object; or determine the identity of the target object in combination with the biological behavior information.

[0139] Optionally, the object identity recognition unit 404 is specifically configured to extract at least one feature indicator value of at least one dimension of device features from the network device fingerprints; and determine the device identity of the corresponding network device according to the feature indicator value.

[0140] Optionally, the dimensions of the device features are divided into physical dimensions, protocol dimensions, and behavior dimensions, wherein: the feature indicators of the physical features of the physical dimensions include carrier frequency offset, modulation error, and signal strength fluctuation standard deviation; the feature indicators of the protocol features of the protocol dimensions include BLE service and Wi-Fi probe; and the feature indicators of the behavior features of the behavior dimensions include time period activity, stay duration, and movement trajectory.

[0141] Optionally, the object identity recognition unit 404 is specifically configured to perform fusion processing on the at least one feature indicator value to obtain a first fusion indicator value of a corresponding dimension, and determine the device identity of the corresponding network device according to the first fusion indicator value of the corresponding dimension.

[0142] Optionally, the object identity recognition unit 404 is specifically configured to determine a current environment scene category, acquire first weights of each first fusion indicator value corresponding to the current environment scene category, or adaptively determine the first weights of each first fusion indicator value; perform fusion processing on each first fusion indicator value according to the first weights to obtain a second fusion indicator value, and determine the device identity of the corresponding network device according to the second fusion indicator value.

[0143] Optionally, the apparatus further comprises a list management unit configured to acquire a network device fingerprint appearing in a network detection environment within a preset time period; determine a device identity of a corresponding network device based on the network device fingerprint; create a management list having a mapping relationship with the network device identity based on the device identity; and add the network device to the corresponding management list.

[0144] Optionally, the list management unit is specifically configured to add a network device fingerprint representing a network device identity that satisfies a first preset condition to a first list; and add a network device fingerprint representing a network device identity that satisfies a second preset condition to a second list.

[0145] Optionally, the apparatus further comprises a fingerprint adding unit configured to add the network device to the corresponding management list in response to a network device adding instruction; and wherein the network device adding instruction comprises a network device fingerprint.

[0146] Optionally, the management list further comprises a temporary list, and the list management unit is specifically configured to add the network device to the temporary list and update a frequency of appearance of the network device; and add the network device to a management list corresponding to a personnel identity in a case where the frequency of appearance is greater than a frequency threshold.

[0147] Optionally, the apparatus further comprises a mode switching unit configured to execute different operation modes based on different management lists.

[0148] The implementation process of the functions and roles of each module in the above apparatus is specifically described in the implementation process of the corresponding steps in the above method, which will not be repeated here.

[0149] For the device embodiment, since it basically corresponds to the method embodiment, the related parts are described in the part of the method embodiment. The above-described device embodiment is only illustrative, and the modules described as separate components can or can not be physically separated, and the components displayed as modules can or can not be physical modules, i.e., they can be located in one place or distributed on multiple network modules. According to actual needs, some or all of the modules can be selected to achieve the purpose of the scheme of the present specification. Those skilled in the art can understand and implement it without creative labor.

[0150] The present specification also provides a computer-readable storage medium having a computer program stored thereon, wherein the program is executed by a processor to implement the steps of the foregoing any one of the identity recognition methods applied to electronic devices provided by the present application.

[0151] In particular, computer readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks.

[0152] The specification also provides a computer program product comprising computer programs / instructions which, when executed by a processor, implement the steps of any of the aforementioned identity recognition methods applied to an electronic device.

Claims

1. An identity recognition method applied to an electronic device, characterized in that, The method comprises: acquiring a network device fingerprint in a network detection environment; the acquisition of the network device fingerprint in the network detection environment is in a non-connected state; the non-connected state is that the electronic device and the network device have not established any valid communication link; determining the identity of the target object based on the network device fingerprint, comprising: identifying the device identity and determining the target object identity based on the network device fingerprint; the network device fingerprint based on the network device fingerprint to identify the device identity and determine the target object identity, further comprising: determining the current connection state between the network device and the electronic device based on the network device fingerprint, determining the target object identity based on the current connection state, and the connection state includes connected and not connected.

2. The method of claim 1, wherein, the network device fingerprint based on the network device fingerprint to identify the device identity, comprising: extracting at least one feature indicator value of at least one dimension of device features from the network device fingerprint; determine the device identity of the corresponding network device according to the feature indicator value.

3. The method of claim 2, wherein, The dimensions of the device features are divided into physical dimensions, protocol dimensions and behavior dimensions, wherein: the feature indicators of the physical features of the physical dimensions include carrier frequency offset, modulation error, signal strength fluctuation standard deviation; the feature indicators of the protocol features of the protocol dimensions include BLE service, Wi-Fi probe; the feature indicators of the behavior features of the behavior dimensions include time period activity, stay time, and movement trajectory.

4. The method of claim 2 or 3, wherein determine the device identity of the corresponding network device according to the feature indicator value, comprising: fuse the at least one feature indicator value to obtain a first fusion indicator value of the corresponding dimension, and determine the device identity of the corresponding network device according to the first fusion indicator value of the corresponding dimension.

5. The method of claim 4, wherein, determine the device identity of the corresponding network device according to the first fusion indicator value of the corresponding dimension, comprising: determine the current environment scene category, obtain the first weight of each first fusion indicator value corresponding to the current environment scene category, or adaptively determine the first weight of each first fusion indicator value; fuse each first fusion indicator value according to the first weight to obtain a second fusion indicator value, and determine the device identity of the corresponding network device according to the second fusion indicator value.

6. The method of claim 1, wherein, The method further comprises: acquiring network device fingerprints appearing in the network detection environment within a preset time period; determine the device identity of the corresponding network device based on the network device fingerprint; create a management list having a mapping relationship with the network device based on the device identity; add the network device to the corresponding management list.

7. The method of claim 6, wherein the network device is added to the corresponding management list, comprising: add the network device fingerprint representing the network device identity that meets the first preset condition to the first list; add the network device fingerprint representing the network device identity that meets the second preset condition to the second list.

8. The method of claim 6, wherein, The method further comprises: In response to a network device adding instruction, adding the network device into a corresponding management list; wherein the network device adding instruction contains a network device fingerprint.

9. The method according to any one of claims 6-8, characterized in that, The management list further includes a temporary list, and the adding the network device into a corresponding management list includes: adding the network device into the temporary list and updating the appearance frequency of the network device; in a case that the appearance frequency is greater than a frequency threshold, adding the network device into a management list corresponding to a personnel identity.

10. The method of claim 6, wherein, The method further includes: executing different operation modes based on different management lists.

11. An identity recognition device applied to an electronic device, characterized in that, The apparatus includes: a network device fingerprint acquisition unit, configured to acquire a network device fingerprint in a network detection environment; the acquiring the network device fingerprint in the network detection environment is performed in a non-connected state; the non-connected state is that the electronic device and the network device have not established any valid communication link; an object identity identification unit, configured to determine an identity of a target object based on the network device fingerprint, including: identifying a device identity and determining a target object identity based on the network device fingerprint; the identifying the device identity and determining the target object identity based on the network device fingerprint further includes: determining a current connection state between the network device and the electronic device based on the network device fingerprint, and determining the target object identity based on the current connection state; the connection state includes connected and not connected.

12. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the program to implement the steps of the method in any one of claims 1-10.

13. A computer readable storage medium having stored thereon a computer program, characterized in that The program is executed by the processor to implement the steps of the method in any one of claims 1-10.

14. A computer program product, characterised in that, including computer programs / instructions, which are executed by the processor to implement the steps of the method in any one of claims 1-10.

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