Fingerprint generation method and device, electronic equipment and storage medium

Abstract

Embodiments of the application disclose a fingerprint generation method and device, electronic equipment and a storage medium. It relates to the technical field of information. The method is applied to electronic equipment and comprises the following steps: generating a digital fingerprint according to physiological data of a user, the digital fingerprint being used to identify identity information of the user. In the scheme, the digital fingerprint used to identify the identity information of the user is generated according to the physiological data of the user. The data used in the process is the physiological data of the user. Since the physiological data of each user is different and is not easy to be stolen by other users, the diversity of the identity information of the user identified by the electronic equipment is increased, and the security of the electronic equipment is improved.

Description

Technical Field

[0001] This application relates to the field of information technology, and in particular to a fingerprint generation method, apparatus, electronic device, and storage medium. Background Technology

[0002] With the development of science and technology, all kinds of electronic devices have appeared in people's daily lives, and people can use electronic devices for entertainment, learning, and so on.

[0003] Currently, various electronic devices typically include user IDs, login accounts, and other information for easy identification. For devices with data encryption capabilities, fingerprints can also be used to directly identify the user. However, because the generation or acquisition of this information is relatively simple, it is easily leaked, posing a security risk. Summary of the Invention

[0004] To address the problems in related technologies, increase the diversity of user identification information on electronic devices, and improve the security of electronic devices, this application provides a fingerprint generation method, apparatus, wearable device, and storage medium. The technical solution is as follows:

[0005] In one aspect, embodiments of this application provide a fingerprint generation method applied to an electronic device, the method comprising:

[0006] A digital fingerprint is generated based on the user's physiological data, and the digital fingerprint is used to identify the user's identity information.

[0007] In another aspect, embodiments of this application provide a fingerprint generation device applied to an electronic device, the device comprising:

[0008] The first generation module is used to generate a digital fingerprint based on the user's physiological data, and the digital fingerprint is used to identify the user's identity information.

[0009] In another aspect, this application provides a wearable device including a processor and a memory, the memory storing a computer program executable on the processor, wherein the processor executes the computer program to implement the fingerprint generation method as described in one aspect above.

[0010] In another aspect, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the fingerprint generation method as described in one of the preceding aspects.

[0011] On the other hand, embodiments of this application provide a computer program product that, when run on a computer, causes the computer to execute to implement the fingerprint generation method as described in one aspect above.

[0012] On the other hand, embodiments of this application provide an application publishing platform for publishing computer program products, wherein when the computer program product is run on a computer, the computer executes to implement the fingerprint generation method as described in one aspect above.

[0013] The beneficial effects of the technical solutions provided in this application include at least the following:

[0014] In electronic devices, digital fingerprints are generated based on users' physiological data. These digital fingerprints are used to identify users' identity information. In this solution, digital fingerprints that identify users' identity information are generated using users' physiological data. Since each user's physiological data is different and not easily stolen by other users, this increases the diversity of user identification information in electronic devices and improves the security of electronic devices. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 A schematic diagram of the structure of an electronic device provided as an exemplary embodiment of this application;

[0017] Figure 2 A flowchart of a fingerprint generation method provided as an exemplary embodiment of this application;

[0018] Figure 3 A flowchart of a fingerprint generation method provided as an exemplary embodiment of this application;

[0019] Figure 4 This is a schematic diagram of a first interface according to an exemplary embodiment of this application;

[0020] Figure 5 This is a schematic diagram of a second interface according to an exemplary embodiment of this application;

[0021] Figure 6 A flowchart of a fingerprint generation method provided as an exemplary embodiment of this application;

[0022] Figure 7 A structural block diagram of a fingerprint generation apparatus provided in an exemplary embodiment of this application;

[0023] Figure 8 This is a schematic diagram of another example of the fingerprint generation device provided in the embodiments of this application. Detailed Implementation

[0024] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0025] In this article, "multiple" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0026] It should be noted that the terms "first, second, third" used in the embodiments of this application are used to distinguish similar or different objects and do not represent a specific order of objects. It can be understood that "first, second, third" can be interchanged in a specific order or sequence where permitted, so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.

[0027] The solution provided in this application can be used in real-world scenarios where people use electronic devices with the function of monitoring and recording users' physiological data in their daily lives, and generate users' digital fingerprints through the monitored users' physiological data. For ease of understanding, some terms and application scenarios involved in the embodiments of this application will be briefly introduced below.

[0028] Digital fingerprint: In this solution, it is used in electronic devices to represent the user's unique identity information. Users can then use digital fingerprints to encrypt various data in electronic devices.

[0029] Physiological data refers to normal physiological indicators of the human body, which are important standards for measuring health. These include body temperature, heart rate, and blood pressure. For example, normal body temperature is 36-37 degrees Celsius, and normal heart rate is 60-100 beats per minute.

[0030] With the development of science and technology, various electronic devices have appeared in people's daily lives, and people can use electronic devices for entertainment, learning, and so on. Among these, in order to facilitate the identification of user identity information, electronic devices usually use various types of information such as user-registered virtual accounts and serial numbers. This information is unique; for example, one virtual account represents one user.

[0031] As people pay more and more attention to their health, some electronic devices have been equipped with functions to monitor human physiological data. For example, wearable devices can monitor a user's heart rate.

[0032] Please refer to Figure 1 This illustrates a schematic diagram of the structure of an electronic device provided in an exemplary embodiment of this application. Figure 1 The electronic device shown includes components such as a processor 110, a memory 120, a transceiver 130, a display unit 140, a sensor 150, and a battery module 160.

[0033] Electronic devices may include, but are not limited to, wearable devices (such as wristbands, smartwatches, smart glasses, or other smart wearable devices that can be worn on a user's body), mobile phones, tablets, laptops, smart glasses, smartwatches, MP3 players (Moving Picture Experts Group Audio Layer III), MP4 players (Moving Picture Experts Group Audio Layer IV), desktop computers, laptop computers, etc.

[0034] The processor 110 is the control center of the electronic device. It connects various parts of the electronic device via various interfaces and lines. By running or executing software programs and / or modules stored in the memory 120, and by calling data stored in the memory 120, it performs various functions and processes data, thereby providing overall monitoring of the electronic device. Optionally, the processor 110 may include one or more processing units; optionally, the processor 110 may integrate an application processor, which mainly handles operating devices, user interfaces, and application programs. Of course, it may also include other processors, which are not listed here.

[0035] The memory 120 can be used to store software programs and modules. The processor 110 executes various functional applications and data processing of the electronic device by running the software programs and modules stored in the memory 120. The memory 120 may mainly include a program storage area and a data storage area. The program storage area may store the operating device and application programs required for at least one function (such as sound playback function, image playback function, etc.); the data storage area may store data created according to the use of the electronic device (such as audio data, telephone book, etc.). In addition, the memory 120 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0036] Transceiver 130 can provide solutions for wireless communication applications in electronic devices, including wireless local area networks (WLANs) (such as wireless fidelity (WiFi) networks), Bluetooth (BT), global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC), and infrared (IR) technologies. Transceiver 130 can be one or more devices integrating at least one communication processing module; for example, integrating an antenna with a baseband processor, or integrating an antenna with a modem processor, etc., without limitation. Optionally, the electronic device can communicate with the aforementioned technologies via its own transceiver 130. Figure 1 The base station establishes a wireless communication connection.

[0037] The display unit 140 can be used to display information input by the user or information provided to the user, as well as various menus of the electronic device. The display unit 140 can be configured in the form of a liquid crystal display (LCD), an organic light-emitting diode (OLED), or the like, and is not limited thereto.

[0038] The electronic device also includes at least one sensor 150, such as a gyroscope sensor, motion sensor, image sensor, and other sensors. The motion sensor may include an accelerometer to detect the magnitude of acceleration in various directions; when stationary, it can detect the magnitude and direction of gravity, and can be used for applications that identify the posture of the electronic device, such as screen orientation switching, related games, and magnetometer posture calibration. Image sensors can be used in modules such as cameras and webcams in the electronic device to collect information about the external scene. Other sensors that may be configured in the electronic device include pressure gauges, barometers, hygrometers, thermometers, infrared sensors, etc., which will not be elaborated here. The electronic device can use the data collected by these sensors during the monitoring of human physiological data.

[0039] The electronic device also includes a battery module 160 that powers the various components. Optionally, the battery module 160 can be logically connected to the processor 110 via a power management device, thereby enabling the power management device to manage functions such as charging, discharging, and power consumption.

[0040] Although not shown, the electronic device may also include a camera. Optionally, the camera may be positioned in the front or rear of the electronic device, and this application embodiment does not limit this.

[0041] It is understood that the structures illustrated in the embodiments of this application do not constitute a specific limitation on the electronic device. In other embodiments of this application, the electronic device may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0042] In addition, those skilled in the art will understand that the structure of the electronic device 100 shown in the above figures does not constitute a limitation on the electronic device 100. The electronic device 100 may include more or fewer components than shown, or combine certain components, or have different component arrangements. For example, the electronic device 100 may also include components such as a microphone, speaker, radio frequency circuit, input unit, audio circuit, WiFi module, battery module, and Bluetooth module, which will not be described in detail here.

[0043] Optionally, for the above Figure 1The electronic device shown can use the aforementioned user identification information to set a password when encrypting stored data or waking up the device. For example, the electronic device can directly identify the user's identity information using their fingerprint. If the user needs to wake up the device, they can input their fingerprint for the device to detect, thus enabling the device to identify the user. However, because the methods for generating or obtaining this information are relatively simple, it is easily leaked and can be impersonated by other users, posing a security risk.

[0044] To address the aforementioned issues in related technologies and increase the diversity of user identification information in electronic devices, this application provides a fingerprint generation method that can generate digital fingerprints based on user physiological data, thereby representing user identity information and improving the security of electronic devices.

[0045] Please refer to Figure 2 This illustration shows a flowchart of a fingerprint generation method provided in an exemplary embodiment of this application, which can be applied to electronic devices. Figure 2 As shown, the fingerprint generation method may include the following steps:

[0046] Step 201: Obtain the user's physiological data.

[0047] Optionally, the electronic device in this application has the function of monitoring and recording the user's physiological data. For example, during user use, the electronic device can automatically monitor the user and obtain the current user's physiological data. For instance, in a wearable device, after the user wears the wearable device, the device can monitor the user based on the heart rate monitoring module, thereby obtaining the user's heart rate as physiological data.

[0048] Optionally, the heart rate here is illustrative. In actual applications, the physiological data that the electronic device can collect may also include one or more of the following: heart rate, blood oxygen concentration, heart rate variability (HRV), wrist temperature, respiratory rate, blood pressure, blood glucose, etc.

[0049] In some embodiments, acquiring user physiological data includes acquiring physiological data obtained by monitoring users using electronic devices. For example, user physiological data can be collected through electronic devices, or through other means (devices), such as mobile phones, health devices, sports devices, other wearable devices, etc., or user physiological data can be collected jointly by electronic devices and other devices; there is no limitation on this.

[0050] Step 202: Generate a digital fingerprint based on the user's physiological data. The digital fingerprint is used to identify the user's identity information.

[0051] Optionally, after acquiring the user's physiological data, the electronic device generates a digital fingerprint based on the monitored physiological data, using the digital fingerprint to identify the user's identity information. Since each user's identity information is different, the digital fingerprint generated by the electronic device for each user is unique.

[0052] In summary, electronic devices acquire users' physiological data and generate digital fingerprints based on this data. These digital fingerprints are used to identify users' identities. This solution uses users' physiological data to generate digital fingerprints that identify them. Since each user's physiological data is unique and not easily stolen by other users, this increases the diversity of user identification information in electronic devices and improves their security.

[0053] The following describes how electronic devices use physiological data collected from users within a preset time period. Based on a hash algorithm, the electronic device takes the feature value of each physiological data point as input and outputs a generated digital fingerprint. This process utilizes physiological data from a preset time period, which is more stable and improves the accuracy of the generated digital fingerprint.

[0054] Please refer to Figure 3 This illustration shows a flowchart of a fingerprint generation method provided in an exemplary embodiment of this application, which can be applied to electronic devices. Figure 3 As shown, the fingerprint generation method may include the following steps:

[0055] Step 301: Obtain the user's physiological data.

[0056] Optionally, the content of the physiological data acquired by the electronic device can be as described in step 201 above, and will not be repeated here.

[0057] Optionally, the electronic device may have a memory to store the monitored physiological data and retrieve the physiological data recorded during a certain period. Alternatively, the electronic device may execute the method flow of this scheme based on all the physiological data recorded by the user.

[0058] In one possible implementation, an electronic device can be used by multiple users. The device can identify different users and store the monitored physiological data according to each user, ensuring that the device can retrieve the physiological data monitored by the same user each time. For example, if user 1 used the electronic device during time period 1, the device will monitor user 1's physiological data; if user 2 used the electronic device during time period 2, the device will also monitor user 2's physiological data. During storage, user 1's physiological data is stored according to their identifier, and user 2's physiological data is stored according to their identifier, preventing data corruption among different users.

[0059] Of course, for ease of processing, an electronic device is usually used primarily by one user, and all physiological data monitored by the electronic device can be considered to belong to one user.

[0060] Optionally, in this solution, the number and types of physiological data acquired can be set by the user. For example, if the electronic device has the function of monitoring 10 types of physiological data, the user can set the electronic device to acquire at least one type of physiological data. When executing the process of this solution, the user can acquire at least one type of physiological data as set.

[0061] Step 302: Based on the physiological data, obtain the feature value corresponding to each type of physiological data. The feature value is used to indicate the physiological data of the user in a preset activity state.

[0062] After acquiring the user's physiological data, the electronic device can further acquire the characteristic value corresponding to each type of physiological data, using this characteristic value to indicate the user's physiological data in a preset activity state. Optionally, the preset activity state can be set by the developer or the user in the electronic device, or it can be determined by the electronic device based on the user's usage information.

[0063] Optionally, a user's activity state can include sleep, exercise, or work. The specific activity state can be set by the developers or the user. In this solution, the electronic device can filter the user's physiological data from the monitored physiological data to identify the user's different states, and then obtain the feature values ​​corresponding to each type of physiological data.

[0064] For example, electronic devices can detect in advance which activity state the user is currently in and store the physiological data according to that activity state during the monitoring process, thereby obtaining physiological data under various states. In this solution, when the electronic device needs to obtain physiological data under a preset activity state, it can filter out the physiological data of the user in that preset activity state from all the physiological data through the preset activity state label.

[0065] In one possible implementation, the electronic device can also determine which physiological data belong to which activity state based on the user's usage information. For example, the electronic device can determine that the user habitually exercises during time slot one each day; therefore, the user's activity state during time slot one is considered to be in an exercise state. Alternatively, the electronic device can determine that the user habitually falls asleep during time slot two each day; therefore, the user's activity state during time slot two is considered to be in a sleep state. It should be noted that the above methods for determining the user's activity state are illustrative in this solution, and the specific process for determining the user's activity state is not limited thereto.

[0066] Optionally, assuming the preset activity state is sleep, the electronic device can acquire the feature value corresponding to each type of physiological data in the following manner: determine the time period of the user's sleep state based on the user's usage information; acquire the corresponding feature value for each type of physiological data according to the time sequence of the user's sleep state time period.

[0067] Optionally, usage information can be user records of their electronic device usage. For example, tracking points can be pre-set in the electronic device to record daily usage patterns. Developers can pre-set tracking points for specific applications on the electronic device to record user habits during use, thus creating a usage log. For instance, users might use their electronic devices more frequently between 8 AM and 10 PM, and typically not between 11 PM and 7 AM; the usage data could be recorded (e.g., application one was used at 9:30 PM, application two at 1:30 PM, etc.).

[0068] For example, electronic devices can determine the time periods during which a user is asleep based on usage information. For instance, in the usage process described above, the electronic device determines whether the user is asleep based on whether they are using the device. Typically, users fall asleep between 11 PM and 7 AM when not using electronic devices; therefore, this time period can be considered the period during which the user is asleep. Each type of physiological data can then be segmented according to the chronological order of this sleep period. All acquired physiological data can be segmented in this way, and the physiological data indicating a sleep state can be filtered out.

[0069] For example, if an electronic device is a wearable device, and a user wears it between 9 and 10 AM every morning to monitor their physiological data, the device can detect that the user's usage information shows they run during this time period. This corresponds to physiological data related to exercise. Therefore, if the preset activity state is "running," the device can determine that the user is in an active state during this time period based on the above method. Alternatively, if a user takes a nap from 12:40 PM to 2 PM every day, the device's determination of the user's sleep state based on their usage information will also include physiological data corresponding to this 12:40 PM to 2 PM timeframe.

[0070] In summary, for the usage records of the aforementioned users, electronic devices use statistical methods to select time periods corresponding to the same daily activity state as the time periods for filtering physiological data.

[0071] Optionally, to ensure more stable physiological data for generating digital fingerprints, the physiological data selected in this step is that of the user while asleep. For example, the electronic device can obtain the time periods during which the user typically does not use the device (when the user is asleep) based on the usage information mentioned above. The electronic device can then filter the physiological data within these time periods and obtain the corresponding feature values ​​for each type of physiological data according to this time period each day.

[0072] In one possible implementation, the electronic device can determine the user's sleep time period in the following manner: For example, the electronic device first determines the user's sleep duration and deep sleep duration within each first preset cycle based on the user's usage information; based on the user's sleep duration and deep sleep duration within each first preset cycle, it selects the user's sleep time period within each first preset cycle. The first preset cycle can be set by the developer or the user themselves within the electronic device. Taking a day as an example, the electronic device can determine the user's sleep and wake-up times each day based on usage information, thereby determining the user's daily sleep duration, while the deep sleep duration can be determined based on the sleep duration.

[0073] For example, for alarm clock applications on electronic devices, by monitoring the user's alarm settings (usage information of the alarm clock application), the user's daily wake-up time can be determined, and the user's daily sleep time can be determined by the last time the user used the electronic device. This allows us to obtain the user's daily sleep duration. Deep sleep duration is a specific period within this sleep duration, for example, a time interval starting M hours after the time of falling asleep and ending N hours before the time of waking up. Here, M and N are integers that can be flexibly set by the user or developers within the electronic device. For instance, if the defined sleep duration is 10 PM to 8 AM, and M=2 and N=3, then the defined deep sleep duration is 12 AM to 5 AM.

[0074] Taking the first preset cycle as an example, the electronic device uses the user's daily sleep duration and deep sleep duration obtained above to find the intersection of the daily sleep duration and deep sleep duration, and then determines the intersection between the daily sleep duration and the daily deep sleep duration. Based on the intersection between the daily sleep duration and the daily deep sleep duration, the electronic device determines the time period during which the user is in a sleep state each day.

[0075] For example, an electronic device determines the current user's daily sleep duration as sleep duration 1, sleep duration 2, sleep duration 3, ..., sleep duration T, and the deep sleep duration as deep sleep duration 1, deep sleep duration 2, deep sleep duration 3, ..., deep sleep duration T, where T is the number of days the user uses the electronic device. That is, for each day, the electronic device can determine the corresponding sleep duration and deep sleep duration, find the intersection of sleep duration 1, sleep duration 2, sleep duration 3, ..., sleep duration T, and find the intersection of deep sleep duration 1, deep sleep duration 2, deep sleep duration 3, ..., deep sleep duration T. The electronic device can then use these two intersections to find another intersection, and use the final time period as the user's sleep time period for each day.

[0076] In one possible implementation, when the electronic device performs the above-described process of acquiring corresponding feature values ​​for each type of physiological data according to the chronological order of the user's sleep time periods, it can do so as follows: According to the chronological order of the user's sleep time periods within each first preset cycle, acquire the mode of each type of physiological data; use the mode of each type of physiological data as the corresponding feature value. Taking a day as an example, the electronic device statistically analyzes each type of physiological data collected during the sleep time periods each day, acquires the mode of each type of physiological data, and uses the acquired mode as the corresponding feature value for each type of physiological data.

[0077] Taking the time period selected above as the user's sleep period as an example, the determined user sleep period is from 1 AM to 4 AM every day. During this time period, the electronic device statistically analyzes each type of physiological data collected. Taking the physiological data including heart rate, blood oxygen saturation, HRV, wrist temperature, and respiratory rate as an example, within the above fixed sleep period, the electronic device collected the following heart rate data: a heart rate of 62 occurred 5 times, a heart rate of 63 occurred 10 times, a heart rate of 64 occurred 30 times, and a heart rate of 65 occurred 5 times. Therefore, the mode of the heart rate data is selected as 64, and 64 is used as the characteristic value corresponding to the heart rate during this fixed sleep period of the day. Other physiological data are similarly obtained. The electronic device can ultimately obtain the characteristic values ​​corresponding to blood oxygen saturation, HRV, wrist temperature, and respiratory rate during the user's sleep period of the day. Therefore, the above-mentioned physiological data is divided into X days, and the characteristic values ​​corresponding to the physiological data for each of the X days will be obtained.

[0078] In one possible implementation, the electronic device acquires the mode for each type of physiological data as follows: It obtains the mode corresponding to the value of each physiological data point using a sliding window approach, where the window length is determined based on the type of each physiological data point. That is, different physiological data points use different window lengths. For example, the electronic device may have different window lengths set for different physiological data points. In acquiring the mode, the window length of the sliding window for each type of physiological data point can be obtained first, and then the mode corresponding to the value of each physiological data point can be acquired using the sliding window approach.

[0079] Taking physiological data, including heart rate, as an example, the sliding window corresponding to heart rate has a window length of 5. During a fixed sleep period on a certain day, the lowest heart rate is 61 and the highest is 72. In the process of calculating the mode, the electronic device counts the total number of data collections from 61 to 65, which is taken as the count for a heart rate of 61. It counts the total number of data collections from 62 to 66, which is taken as the count for a heart rate of 62, and so on, until all the counts under the sliding window length are obtained. From these, the mode is selected as the feature value of the heart rate.

[0080] If the physiological data is respiratory rate, and the sliding window corresponding to the respiratory rate has a window length of 3, the lowest respiratory rate during a fixed sleep period on a certain day is 16 breaths per minute, and the highest is 20 breaths per minute. In the process of calculating the mode, the electronic device counts the total number of data collections from 16 to 18, which is taken as the count for the respiratory rate of 16 breaths per minute. It counts the total number of data collections from 17 to 19, which is taken as the count for the respiratory rate of 17 breaths per minute, and so on, until all the counts under the sliding window length are obtained. From these, the mode is selected as the feature value of the respiratory rate.

[0081] In one possible implementation, the window length of the aforementioned sliding window can also be determined based on the difference between the maximum and minimum values ​​of each physiological data point. For example, in addition to using the window length based on a pre-determined type of each physiological data point, electronic devices can also use the difference between the maximum and minimum values ​​of each currently calculated physiological data point, and use one-quarter of that difference (rounded to the nearest integer if there is a decimal) as the window length of the sliding window. For example, if the minimum heart rate is 60 and the maximum is 75, the window length of the sliding window corresponding to heart rate is (75-60) / 4 = 3. If the minimum wrist temperature is 31 degrees and the maximum is 34 degrees, the window length of the sliding window corresponding to wrist temperature is (34-31) / 4 = 1.

[0082] Of course, using the mode of physiological data as a feature value is an example. In practical applications, other data such as the average, maximum or minimum value of physiological data can also be selected as feature values. This solution does not limit this.

[0083] In one possible implementation, the electronic device has a display screen, and the window length of the aforementioned sliding window can also be set by a user triggering a process on the display screen. For example, a first interface is displayed on the display screen, which includes a control for setting the window length used by the sliding window; in response to a triggering operation on the setting control, the window length used by the sliding window is set.

[0084] Please refer to Figure 4 This illustrates a schematic diagram of a first interface according to an exemplary embodiment of this application. Figure 4 As shown, the device includes a first interface 401, various physiological data 402, various setting controls 403, an add control 404, and a save control 405. Users can perform a series of operations on the electronic device to display the first interface 401 and edit the setting controls 403 corresponding to the various physiological data 402. In response to triggering an operation on a setting control, the user can input a corresponding length and trigger the save control 405, thereby setting the window length of the sliding window corresponding to each physiological data. Users can also trigger the add control 404 to add and set the corresponding window length for other physiological data.

[0085] Optionally, in the aforementioned electronic device with a display screen, the method for determining the time period corresponding to the preset activity state, besides the method based on user information given in this embodiment, can also be directly set by the user. For example, the electronic device can display a second interface on the display screen, which includes an input control for the preset time period and a setting control for the preset activity state; and in response to the triggering operation of the input control and the setting operation of the setting control, the time period input by the user is obtained as the preset time period, and the set activity state is used as the preset activity state.

[0086] Please refer to Figure 5 This illustrates a schematic diagram of a second interface according to an exemplary embodiment of this application. Figure 5 As shown, the device includes a second interface 501, an input control 502 for a preset time period, and a preset activity state 503. Users can perform a series of operations on the electronic device to display the second interface 501, edit input in the input control 502 for the preset time period, and input the desired activity state in the preset activity state 503. In other words, in response to a trigger operation on the input control 502, the user can input a custom time period, thus setting it as a preset time period and a preset activity state. For example, if the user inputs a preset activity state of sleep, and the corresponding preset time period is from 2 AM to 5 AM daily, then during the feature value acquisition process, the corresponding feature values ​​for each physiological data point are acquired according to this preset time period.

[0087] Step 303: Generate a digital fingerprint based on the set of numerical values ​​composed of the feature values ​​corresponding to each type of physiological data. The digital fingerprint is used to identify the user's identity information.

[0088] Optionally, the electronic device can generate a digital fingerprint as follows: The set of feature values ​​corresponding to each type of physiological data is hashed using a hash algorithm to obtain a fixed-length hash value; this fixed-length hash value is then used as the digital fingerprint. The hash algorithm can be pre-set in the electronic device by the developers. In this solution, the hash algorithm used to generate the digital fingerprint can be defined by the developers themselves and is not limited here. For example, the feature values ​​corresponding to each type of physiological data obtained above are used as input to the hash algorithm. Through the calculation of the hash algorithm, a fixed-length hash value is obtained, and the electronic device uses the generated hash value as the digital fingerprint identifying the user's identity information.

[0089] Taking a first preset period of days as an example, through step 302 above, corresponding feature values ​​are obtained for various physiological data monitored each day. Assuming that 30 days' worth of physiological data corresponding to these feature values ​​are obtained, these feature values ​​are input into a hash algorithm. The hash algorithm calculates a digital fingerprint with personal identification information. For example, the physiological data includes five indicators: heart rate, blood oxygen concentration, HRV, wrist temperature, and respiratory rate. The final set of input values ​​from the electronic device to the hash algorithm includes the feature values ​​of heart rate (30 heart rate corresponding feature values), blood oxygen concentration (30 blood oxygen concentration corresponding feature values), HRV (30 HRV corresponding feature values), wrist temperature (30 wrist temperature corresponding feature values), and respiratory rate (30 respiratory rate corresponding feature values) for each day of the 30 days. Each feature value in the set can be considered an element, and the set of values ​​is equivalent to an array.

[0090] In some embodiments, the process of generating a digital fingerprint based on a user's physiological data by an electronic device may include: acquiring feature values ​​of some or all physiological data under a preset activity state; and generating a digital fingerprint based on a set of values ​​composed of some or all feature values. It is understood that step 302 above may involve acquiring feature values ​​of some or all physiological data under a preset activity state. The physiological data collected under these preset activity states is more stable. For example, in addition to the sleep state mentioned above, the preset activity state may also include: a resting state, a non-awake state, a light sleep state, or a deep sleep state. Related descriptions can be found in the descriptions of other embodiments of this application and will not be repeated here.

[0091] In this approach, step 303 above can be used to generate a digital fingerprint based on a numerical set composed of some or all feature values. In some embodiments, taking a sleep state as an example, obtaining feature values ​​of some or all physiological data under the preset activity state includes: during a preset sleep period, obtaining the mode of each partial or complete physiological data item as a feature value through a sliding window.

[0092] The method for determining the mode as a feature value can be found in the descriptions of other embodiments, and will not be repeated here.

[0093] Optionally, in this embodiment, generating a digital fingerprint based on a numerical set composed of some or all of the feature values ​​includes: using some or all of the feature values ​​to generate a digital fingerprint with a personal identifier through a hash algorithm. The method for generating the digital fingerprint can be referred to in other embodiments and will not be repeated here.

[0094] In one possible implementation, the electronic device is equipped with a second preset period. This second preset period indicates the cycle in which the electronic device updates the user's digital fingerprint. The electronic device can also, according to the second preset period, reacquire various physiological data obtained from monitoring the user during the fingerprint update cycle, and generate a new digital fingerprint for the user based on this data. This new digital fingerprint is then used to update the previously stored digital fingerprint. In other words, the electronic device can periodically update the generated digital fingerprint by replacing the previously generated one with a new one. The second preset period can also be set by the developers or the user within the electronic device.

[0095] For example, the second preset period is one month. This means that every month, the electronic device can generate a new digital fingerprint based on the user's physiological data monitored within that month, thus more accurately reflecting the user's physical condition. Of course, the second preset period can also be two months, three months, or a longer period of time; it is not limited here.

[0096] Taking a second preset period of one month, and the preset activity state as the user's daily sleep state, corresponding to the sleep time period from 2 AM to 5 AM, as an example, for the process described in steps 301 to 303 above, after acquiring the monitored physiological data for one month, the electronic device can select various physiological data monitored when the user is asleep, according to the preset time period. It then obtains feature values ​​for each physiological data point from 2 AM to 5 AM each day, and finally inputs these feature values ​​into a hash algorithm to generate a digital fingerprint with a personal identifier, thereby identifying the individual's identity information. The above steps can be repeated monthly to update the user's digital fingerprint in a timely manner.

[0097] Of course, in one possible implementation, the physiological data used to generate the digital fingerprint could be obtained from monitoring within various preset time periods in historical records. For example, during a user's use of an electronic device, a digital fingerprint could be generated for the user every month, starting from the first time the user uses the device. In the process of generating a new digital fingerprint in the second month, physiological data from the previous two months within preset time periods could be used to generate the digital fingerprint, thereby achieving an effect that is more consistent with the user's physical condition.

[0098] In one possible implementation, after the electronic device generates a digital fingerprint for the user based on physiological data, it can further encrypt the target data using this fingerprint when storing the target data. The target data has a higher security level than a preset level, which can be set by the user. For example, in an electronic device, the data to be stored can be categorized according to security levels, with important data naturally having a higher security level and less important data having a lower security level.

[0099] Optionally, during the process of a user storing target data in an electronic device, the device can automatically encrypt the target data using a generated digital fingerprint, thereby ensuring the security of the target data. Assuming the electronic device has three security levels—low, medium, and high—with the default level being medium, for a specific target data with a high security level, if the user wants to store that data, and the electronic device obtains that the security level of the target data is high (higher than the default level), it can use the previously generated digital fingerprint to encrypt the target data. Taking a smartwatch as an example, data such as user images and bank passwords recorded on the smartwatch can be considered high-security data. If the smartwatch needs to store this data, it can use the aforementioned generated digital fingerprint for encryption.

[0100] It should be noted that the confidentiality level of the target data can be determined by the electronic device based on manual input by the user or by information about the target data itself; there is no limitation here. Furthermore, in addition to automatic encryption, users can also manually encrypt the stored data, choosing to use digital fingerprint encryption to ensure data storage security.

[0101] In one possible implementation, the electronic device may also perform the following steps: when it is necessary to decrypt encrypted target data, collect data on the user currently using the electronic device according to the type of physiological data used to generate the digital fingerprint; generate a decryption fingerprint based on the currently collected physiological data; and decrypt the target data if the decryption fingerprint is the same as the digital fingerprint.

[0102] Optionally, if the user needs to decrypt target data previously encrypted using a digital fingerprint, the electronic device needs to collect its current physiological data and generate a decryption fingerprint based on this data. The decryption fingerprint, like the digital fingerprint, is generated using a hash algorithm. If the decryption fingerprint matches the digital fingerprint, the target data can be decrypted; otherwise, it cannot. It's important to note that the amount of physiological data collected is the same as the amount used to generate the digital fingerprint previously. For example, in the biochemical digital fingerprinting process, a one-month (30-day) cycle is used. Therefore, the currently collected physiological data includes both the current data and data from the previous 29 days. The process involves obtaining feature values ​​using the same method described above, and finally, a new decryption fingerprint is generated using a hash algorithm. The entire process is similar to that of generating a mathematical fingerprint and will not be elaborated upon here.

[0103] It should be noted that, in addition to being executed by electronic devices, the steps of this solution can also be performed by sending these physiological data to a server or the cloud, allowing the server or other devices with greater data processing capabilities to execute the method of this solution. This solution is not limited to either of these methods.

[0104] In summary, in electronic devices, physiological data obtained from monitoring users is acquired; based on this physiological data, a digital fingerprint is generated, which is used to identify the user's identity information. In this solution, the digital fingerprint identifying the user's identity information is generated from the user's physiological data. This process uses physiological data obtained from the electronic device's monitoring of the user. Because each user's physiological data is different and not easily stolen by other users, the diversity of user identification information on the electronic device is increased, thus improving the security of the electronic device.

[0105] In addition, this solution selects the mode of physiological data as the feature value by using a sliding window. Compared with the ordinary method of selecting the mode, the feature value selected by the sliding window is more representative of the user's physiological data within the preset time period, thus improving the reliability of generating digital fingerprints.

[0106] Below, taking the aforementioned electronic device as a wearable device as an example, wearable devices can monitor users' physiological data in daily life. The method for generating digital fingerprints in this solution can be as follows: Please refer to... Figure 6 This illustration shows a flowchart of a fingerprint generation method provided in an exemplary embodiment of this application, which can be applied to wearable devices. Figure 6 As shown, the fingerprint generation method may include the following steps:

[0107] Step 601: The wearable device monitors the user and acquires various physiological data.

[0108] Optionally, wearable devices can be smartwatches, smart bracelets, or similar devices that monitor various physiological data of the user while the user is wearing them. These data can include heart rate, blood oxygen saturation, HRV, wrist temperature, and respiratory rate.

[0109] Step 602: The wearable device obtains the user's regular sleep period based on the user's sleep duration and deep sleep duration.

[0110] Optionally, this is equivalent to the process by which a wearable device determines the time period corresponding to the user's sleep state based on the user's usage information. In this embodiment, the usage information includes the user's daily routine, namely the user's sleep duration and deep sleep duration. Based on the sleep duration and deep sleep duration, the user's regular sleep period is obtained (i.e., the user is in a sleep state during this time period every day).

[0111] Step 603: Select physiological data that falls within the aforementioned sleep period from the various physiological data monitored.

[0112] Optionally, wearable devices can filter and acquire physiological data monitored during the user's sleep. Since data measured during rest is relatively stable, physiological data during sleep is more representative of the user's physiological data.

[0113] Step 604: For each sleep period, obtain the mode of each physiological data point as the feature value using a sliding window.

[0114] Optionally, the wearable device may obtain the mode based on the aforementioned sliding window method as the feature value of each physiological data.

[0115] Step 605: Using the obtained feature values, generate a digital fingerprint with a personal identifier through a hash algorithm.

[0116] Optionally, the obtained feature values ​​can be aggregated into an array and used as input to a hash algorithm. The digital fingerprint generated based on the hash algorithm can represent an individual's identity information and has a personal identification function.

[0117] Step 606: Repeat the above steps on a monthly basis to update the generated digital fingerprint.

[0118] Optionally, wearable devices can update the user's digital fingerprint monthly to generate a corresponding digital fingerprint based on the user's latest physical condition.

[0119] In summary, wearable devices already have the function of monitoring and collecting users' physiological data. This solution can add a new application, which will complement each other. The generated digital fingerprint can be used to encrypt certain sensitive or high-density data on the device, increase the diversity of user identification information of wearable devices, and improve the security of wearable devices.

[0120] The following are embodiments of the apparatus described in this application, which can be used to execute the embodiments of the method described in this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the method described in this application.

[0121] Please refer to Figure 7 This illustration shows a structural block diagram of a fingerprint generation apparatus provided in an exemplary embodiment of this application. The fingerprint generation apparatus 700 can be used in an electronic device to perform all or part of the steps executed by the electronic device in the methods provided in the various embodiments shown above. The fingerprint generation apparatus 700 includes:

[0122] The first acquisition module 701 is used to acquire physiological data obtained from monitoring users of the electronic device;

[0123] The first generation module 702 is used to generate a digital fingerprint based on the user's physiological data, and the digital fingerprint is used to identify the user's identity information.

[0124] In summary, in electronic devices, digital fingerprints are generated based on users' physiological data, and these digital fingerprints are used to identify users' identity information. In this solution, digital fingerprints are generated using users' physiological data to identify them. Since each user's physiological data is different and not easily stolen by other users, this increases the diversity of user identification information in electronic devices and improves the security of the electronic devices.

[0125] Optionally, the first generation module 702 includes: a first acquisition unit and a first generation unit;

[0126] The first acquisition unit is used to acquire a feature value corresponding to each type of physiological data based on the physiological data, and the feature value is used to indicate the physiological data of the user in a preset activity state;

[0127] The first generation unit is used to generate the digital fingerprint based on the feature value corresponding to each type of physiological data.

[0128] Optionally, the first generation module 702 includes: a second acquisition unit and a second generation unit;

[0129] The second acquisition unit is used to acquire feature values ​​of some or all physiological data under a preset activity state;

[0130] The second generation unit is used to generate the digital fingerprint based on a numerical set composed of some or all of the feature values.

[0131] Optionally, the preset activity state includes: sleep state, rest state, non-awake state, light sleep state, or deep sleep state.

[0132] Optionally, the second acquisition unit is further configured to: acquire, during a preset sleep period, the mode of each of the partial or all physiological data items as a feature value via a sliding window; and / or,

[0133] The second generating unit is further configured to:

[0134] Using some or all of the aforementioned feature values, a digital fingerprint with a personal identifier is generated through a hash algorithm.

[0135] Optionally, the preset activity state is a sleep state, and the first acquisition unit further includes: a first determination subunit and a first acquisition subunit;

[0136] The first determining subunit is used to determine the time period of the user in the sleep state based on the user's usage information;

[0137] The first acquisition subunit is used to acquire the corresponding feature value for each type of physiological data according to the time sequence of the user's time period in the sleep state.

[0138] Optionally, the first determining subunit is further configured to:

[0139] Based on the user's usage information, determine the user's sleep duration and deep sleep duration in each first preset cycle;

[0140] Based on the user's sleep duration and deep sleep duration in each first preset cycle, select the time period in the user's sleep state within each first preset cycle.

[0141] Optionally, the first acquisition subunit is further configured to:

[0142] According to the time sequence of the time period when the user is in the sleep state in each first preset cycle, obtain the mode of each physiological data;

[0143] The mode of each physiological data point is used as the feature value corresponding to each physiological data point.

[0144] Optionally, obtaining the mode of each of the physiological data includes:

[0145] The mode corresponding to the value of each physiological data is obtained by means of a sliding window, wherein the window length of the sliding window is determined based on the type of each physiological data.

[0146] Optionally, the first generating unit is further configured to:

[0147] For each type of physiological data, the feature value corresponding to it is used to obtain a hash value of fixed length using a hash algorithm.

[0148] The fixed-length hash value is used as the digital fingerprint.

[0149] Optionally, the electronic device is provided with a second preset period, which indicates the period at which the electronic device updates the user's digital fingerprint. The device further includes:

[0150] The reacquisition module is used to reacquire various physiological data obtained by the electronic device from monitoring the user of the electronic device within the fingerprint update cycle according to the second preset cycle, generate a new digital fingerprint for the user based on the various physiological data, and update the previously stored digital fingerprint using the new digital fingerprint.

[0151] Optionally, the device further includes:

[0152] The first encryption module is used to encrypt the target data based on the digital fingerprint when storing the target data after generating the digital fingerprint according to the user's physiological data, wherein the target data is data with a confidentiality level higher than a preset level; and / or,

[0153] The first acquisition module is used to collect data on the user currently using the electronic device according to the type of physiological data that generated the digital fingerprint when it is necessary to decrypt the encrypted target data.

[0154] The third generation module is used to generate a decryption fingerprint based on the currently collected physiological data;

[0155] The first encryption module is used to decrypt the target data if the decryption fingerprint is the same as the digital fingerprint.

[0156] Please refer to Figure 8 This is a schematic diagram illustrating another example of the fingerprint generation device provided in this application embodiment. The fingerprint generation device 800 can be a wearable device capable of implementing the functions of the method provided in this application embodiment. The fingerprint generation device 800 can be a chip system. In this application embodiment, the chip system can be composed of chips or may include chips and other discrete components.

[0157] In terms of hardware implementation, the aforementioned communication module can be a transceiver, which is integrated into the fingerprint generation device 800 to form the communication interface 803.

[0158] The fingerprint generation device 800 includes at least one processor 801, used to implement or support the fingerprint generation device 800 in implementing the functions of the wearable device in the methods provided in the embodiments of this application. Exemplarily, the processor 801 can perform steps such as acquiring physiological data obtained from monitoring a user using the electronic device; generating a digital fingerprint based on the user's physiological data, the digital fingerprint being used to identify the user's identity information, etc. For details, please refer to the detailed description in the method examples, which will not be repeated here.

[0159] The fingerprint generation device 800 may further include at least one memory 802 for storing program instructions and / or data. The memory 802 is coupled to the processor 801. The coupling in this embodiment is an indirect coupling or communication connection between devices, units, or modules, and may be electrical, mechanical, or other forms, for information exchange between devices, units, or modules. The processor 801 may operate in conjunction with the memory 802. The processor 801 may execute program instructions stored in the memory 802. At least one of the at least one memory may be included in the processor.

[0160] The fingerprint generating device 800 may further include a communication interface 803 for communicating with other devices via a transmission medium, thereby enabling the devices in the fingerprint generating device 800 to communicate with other devices. For example, the other device may be a network-side device. The processor 801 may use the communication interface 803 to send and receive data. Specifically, the communication interface 803 may be a transceiver.

[0161] This application embodiment does not limit the specific connection medium between the communication interface 803, processor 801, and memory 802. This application embodiment... Figure 8 The memory 802, processor 801, and communication interface 803 are connected via a bus 804. Figure 8 The connections between other components are shown in bold lines only and are not intended to be limiting. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, Figure 8 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0162] In the embodiments of this application, the processor 801 may be a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field-programmable gate array or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component, capable of implementing or executing the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor may be a microprocessor or any conventional processor, etc. The steps of the methods disclosed in the embodiments of this application can be directly manifested as being executed by a hardware processor, or being executed by a combination of hardware and software modules in the processor.

[0163] In this embodiment, the memory 802 can be non-volatile memory, such as a hard disk drive (HDD) or a solid-state drive (SSD), or it can be volatile memory, such as random-access memory (RAM). Memory is any other medium capable of carrying or storing desired program code in the form of instructions or data structures, and accessible by a computer, but is not limited thereto. The memory in this embodiment can also be a circuit or any other device capable of implementing storage functions, used to store program instructions and / or data.

[0164] Optionally, embodiments of this application also provide an electronic device, which includes a memory and a processor. The memory stores a computer program that can run on the processor. When the processor executes the computer program, it implements all or part of the steps performed by the electronic device in the fingerprint generation methods of the above embodiments.

[0165] Optionally, embodiments of this application also provide a computer-readable medium having a computer program stored thereon, which, when executed by a processor, implements all or part of the steps performed by an electronic device in the fingerprint generation methods of the various embodiments described above.

[0166] Optionally, embodiments of this application also provide a chip containing an executable computer program. When the chip executes the computer program, it implements all or part of the steps performed by the electronic device in the fingerprint generation methods of the above embodiments.

[0167] Optionally, embodiments of this application also provide a computer program product that, when run on a computer, causes the computer to execute all or part of the fingerprint generation methods described in the above embodiments, performed by an electronic device.

[0168] Optionally, embodiments of this application also provide an application publishing platform for publishing computer program products, wherein when the computer program product is run on a computer, the computer executes all or part of the fingerprint generation methods of the above embodiments, performed by an electronic device.

[0169] It should be noted that the device provided in the above embodiments is only illustrated by the division of the above functional modules when controlling electronic devices. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiments belong to the same concept, and their specific implementation process can be found in the method embodiments, which will not be repeated here.

[0170] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0171] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0172] The above description is merely an optional embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A fingerprint generation method, characterized in that, Applied to electronic devices, the method includes: A digital fingerprint is generated based on the user's physiological data, and the digital fingerprint is used to identify the user's identity information.

2. The method according to claim 1, characterized in that, The step of generating a digital fingerprint based on the user's physiological data includes: Based on the physiological data, a feature value corresponding to each type of physiological data is obtained. The feature value is used to indicate the physiological data of the user in a preset activity state. The digital fingerprint is generated based on the set of numerical values ​​composed of the feature values ​​corresponding to each type of physiological data.

3. The method according to claim 1, characterized in that, The step of generating a digital fingerprint based on the user's physiological data includes: Acquire feature values ​​of some or all physiological data under a preset activity state; The digital fingerprint is generated based on a set of values ​​consisting of some or all of the aforementioned feature values.

4. The method according to claim 2 or 3, characterized in that, The preset activity states include: sleep state, still state, non-awake state, light sleep state, or deep sleep state.

5. The method according to claim 4, characterized in that, The step of obtaining feature values ​​of some or all physiological data under the preset activity state includes: during a preset sleep period, obtaining the mode of each of the partial or all physiological data items as a feature value through a sliding window; and / or, Generating the digital fingerprint based on a numerical set composed of some or all of the feature values ​​includes: Using some or all of the aforementioned feature values, a digital fingerprint with a personal identifier is generated through a hash algorithm.

6. The method according to claim 2, characterized in that, The preset activity state is a sleep state. The step of obtaining the feature value corresponding to each type of physiological data based on the physiological data includes: Based on the user's usage information, determine the time period during which the user is in the sleep state; According to the time sequence of the user's sleep state, the corresponding feature value is obtained for each type of physiological data.

7. The method according to claim 6, characterized in that, Determining the time period of the user in the sleep state based on the user's usage information includes: Based on the user's usage information, determine the user's sleep duration and deep sleep duration in each first preset cycle; Based on the user's sleep duration and deep sleep duration in each first preset cycle, select the time period in the user's sleep state within each first preset cycle.

8. The method according to claim 6, characterized in that, The step of obtaining corresponding feature values ​​for each type of physiological data according to the time sequence of the user's sleep state includes: According to the time sequence of the time period when the user is in the sleep state in each first preset cycle, obtain the mode of each physiological data; The mode of each physiological data point is used as the feature value corresponding to each physiological data point.

9. The method according to claim 8, characterized in that, Obtaining the mode of each type of physiological data includes: The mode corresponding to the value of each physiological data is obtained by means of a sliding window, wherein the window length of the sliding window is determined based on the type of each physiological data.

10. The method according to claim 2, characterized in that, The step of generating the digital fingerprint based on the feature value corresponding to each type of physiological data includes: The set of numerical values ​​corresponding to the feature values ​​of each type of physiological data is processed using a hash algorithm to obtain the hash value; The hash value is used as the digital fingerprint.

11. The method according to any one of claims 1 to 10, characterized in that, The electronic device is equipped with a second preset period, which indicates the period during which the electronic device updates the user's digital fingerprint. The method further includes: According to the second preset cycle, the electronic device reacquires various physiological data obtained by monitoring the user of the electronic device during the fingerprint update cycle, generates a new digital fingerprint for the user based on the various physiological data, and updates the previously stored digital fingerprint with the new digital fingerprint.

12. The method according to any one of claims 1 to 10, characterized in that, After generating a digital fingerprint for the user based on the user's physiological data, the method further includes: When storing target data, the target data is encrypted based on the digital fingerprint, and the target data has a confidentiality level higher than a preset level; and / or, When it is necessary to decrypt encrypted target data, the user currently using the electronic device is collected according to the type of physiological data that generated the digital fingerprint; Generate a decryption fingerprint based on the currently collected physiological data; If the decryption fingerprint is the same as the digital fingerprint, the target data is decrypted.

13. A fingerprint generation device, characterized in that, Applied to electronic devices, the device includes: The first generation module is used to generate a digital fingerprint based on the user's physiological data, and the digital fingerprint is used to identify the user's identity information.

14. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program that can run on the processor, and the processor executing the computer program to implement the fingerprint generation method according to any one of claims 1 to 12.

15. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the fingerprint generation method as described in any one of claims 1 to 12.

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