Operating methods, apparatus, devices, media, and programs of wireless communication modules

Abstract

This specification provides a method, apparatus, device, medium, and program for operating a wireless communication module. The method includes: acquiring historical detection information obtained by the wireless communication module detecting target objects within a network detection environment over a historical time period; predicting the future appearance time of the target objects within the network detection environment based on the historical detection information; and switching the operating mode of the wireless communication module in response to a time period prior to the future appearance time.

Description

Technical Field

[0001] This specification relates to the field of wireless communication technology, and in particular to the operation methods, apparatus, devices, media, and programs of wireless communication modules. Background Technology

[0002] Currently, smart door locks, community monitoring systems, and other electronic security devices are widely used in communities and homes. Their core function is to identify the whereabouts of strangers and prevent unauthorized entry, thereby protecting the personal and property safety of residents. It is worth noting that these devices generally integrate biometric information collection modules: in addition to basic facial recognition, some high-end devices can even capture the physiological characteristics of surrounding organisms (such as heart rate and body posture).

[0003] However, for community or household members, there is a demand for electronic security devices that can monitor the movements of strangers, protect the biometric information privacy of themselves or certain neighbors, and prevent data leakage from their devices. General technologies for identifying individuals require electronic devices to establish a communication connection with the target to obtain relevant information and determine the target's identity. However, establishing a communication connection with the target carries the risk of leaking the privacy information of the electronic device.

[0004] With the development of IoT technology, wireless communication modules are widely used in electronic security equipment to detect objects in the network detection environment and identify the identity of the object based on the detection information. This allows the electronic security equipment to switch operating modes according to the identity of the object in order to protect the privacy and security of the specific object.

[0005] Meanwhile, due to the need for real-time detection, wireless communication modules are generally kept on continuously; however, because the frequency of effective events within a specific time period is low, the module is mostly in an idle mode where it does not participate in effective data interaction, resulting in high energy consumption in this working mode. Summary of the Invention

[0006] To overcome the problems existing in related technologies, this specification provides methods, apparatus, devices, media and programs for operating wireless communication modules.

[0007] According to a first aspect of the embodiments of this specification, a method for operating a wireless communication module is provided, applied to an electronic device having a bioinformation acquisition module and a wireless communication module, the method comprising:

[0008] Acquire historical detection information obtained by the wireless communication module from detecting target objects in the network detection environment within a historical time period;

[0009] Predict the future appearance time of the target object within the network detection environment based on the historical detection information;

[0010] The operating mode of the wireless communication module is switched in response to a time period of a first preset duration before the future occurrence time.

[0011] According to a second aspect of the embodiments of this specification, an operating apparatus for a wireless communication module is provided, the apparatus comprising:

[0012] The acquisition unit is used to acquire historical detection information obtained by the wireless communication module from detecting target objects in the network detection environment within a historical time period.

[0013] The prediction unit is used to predict the future appearance time of the target object within the network detection environment based on the historical detection information.

[0014] The control unit is used to switch the operating mode of the wireless communication module in response to a time period of a first preset duration before the future occurrence time.

[0015] According to a third aspect of the embodiments of this specification, an electronic device is provided, including a wireless communication module, a biometric identification module, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method as described in the first aspect.

[0016] According to a fourth aspect of the embodiments of this specification, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the method as described in the first aspect.

[0017] According to a fifth aspect of the embodiments of this specification, a computer program product is provided, including a computer program / instructions that, when executed by a processor, implement the steps of the method as described in the first aspect.

[0018] The technical solutions provided by the embodiments of this specification may include the following beneficial effects: Based on historical detection information, this solution predicts the future appearance time of the target object in the network detection environment, and switches the operating mode of the wireless communication module in the dormant state to the normal operating state in advance for a first preset time.

[0019] This demonstrates that the system switches the operating mode of the wireless communication module only when necessary, keeping it in sleep mode the rest of the time, significantly reducing energy consumption. Furthermore, the timing of mode switching is predicted based on historical data, effectively matching the expected appearance time of the target object and greatly reducing invalid wake-ups.

[0020] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this specification. Attached Figure Description

[0021] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this specification and, together with the description, serve to explain the principles of this specification.

[0022] Figure 1 This is a schematic diagram illustrating a scenario for identifying the identity of a target object according to an exemplary embodiment.

[0023] Figure 2 This is a flowchart illustrating an operation method of a wireless communication module according to an exemplary embodiment of this specification.

[0024] Figure 3 This is a schematic diagram illustrating, according to an exemplary embodiment, a method for determining the identity of a target object based on indicator feature values.

[0025] Figure 4 This is a schematic diagram of the structure of an electronic device according to an exemplary embodiment of this specification.

[0026] Figure 5 This is a block diagram illustrating an operating apparatus for a wireless communication module according to an exemplary embodiment of this specification. Detailed Implementation

[0027] 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 numerals 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 specification. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this specification as detailed in the appended claims.

[0028] The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of this specification. The singular forms “a,” “the,” and “the” as used in this specification and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any and all possible combinations of one or more of the associated listed items.

[0029] It should be understood that although the terms first, second, third, etc., may be used in this specification to describe various information, this information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this specification, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0030] Currently, smart door locks, community monitoring systems, and other electronic security devices are widely used in communities and homes. Their core function is to identify the whereabouts of strangers and prevent unauthorized entry, thereby protecting the personal and property safety of residents. It is worth noting that these devices generally integrate biometric information collection modules: in addition to basic facial recognition, some high-end devices can even capture the physiological characteristics of surrounding organisms (such as heart rate and body posture).

[0031] However, for members of a community or family, there is a demand for electronic security devices that can monitor the whereabouts of strangers, to protect the privacy of their own or certain neighbors' biometric information, and residents also want to avoid data leakage from their devices.

[0032] Electronic security devices can be electronic devices equipped with biometric data acquisition modules. Examples include surveillance equipment and smart door locks. These biometric data acquisition modules can be facial recognition modules, behavioral biometric data acquisition modules, physiological biometric data acquisition modules, and so on.

[0033] 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.

[0034] With the development of IoT technology, wireless communication modules are being applied to the aforementioned electronic devices to detect objects within the network environment and identify their identities based on the detected information. This allows the electronic device to switch operating modes according to the object's identity, protecting the privacy and security of specific objects. Of course, the electronic device used in this solution may not have a built-in biometric data acquisition module. For example, if necessary, the electronic device can acquire biometric information from other biometric data acquisition devices.

[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 identifying the identity of the target object, the electronic device can automatically switch to the operating mode corresponding to that identity. This solution does not limit the specific content and method of the electronic device switching the operating mode after identifying the target object.

[0038] like Figure 1 As shown, taking a home door monitoring application scenario as an example, the electronic device can be a smart door lock 10 equipped with a biometric information acquisition module and a wireless communication module. The wireless communication module of the smart door lock 10 can detect target objects 11 appearing within the network detection environment 12 to obtain detection information, determine the identity of the target object 11 based on the detection information, and finally switch to an operating mode corresponding to the identified target object 11's identity. For example, the types of target object 11 that the smart door lock 10 can identify include, but are not limited to, family members, neighbors, and other people. After identifying family members or neighbors, the smart door lock 10 can take privacy protection measures to protect their biometric privacy information from being leaked.

[0039] To ensure real-time detection, wireless communication modules are generally kept on continuously. However, in scenarios with sparse human activity, these modules spend most of their time idle, leading to high energy consumption. For example, in the scenario of home door monitoring, the number of times a target appears near the door in a day is very limited. When no target appears, the wireless communication module does not need to detect the surrounding environment and can remain in sleep mode to save energy.

[0040] The conventional approach is to use timed intervals for sleep mode to save energy, for example, by repeatedly switching between operating and sleep modes. However, this method cannot adapt to the sudden appearance of a target. For example, if it happens to appear during the sleep mode, the appropriate detection opportunity may be missed because the wireless communication module is not activated in time.

[0041] To address the aforementioned technical problems, this specification provides a method for operating a wireless communication module, which accurately determines the switching timing while reducing the power consumption of the wireless communication module.

[0042] The embodiments described in this specification will now be described in detail.

[0043] Figure 2 This is a flowchart illustrating an operational method of a wireless communication module according to an exemplary embodiment. Figure 2 As shown, this method can be applied to electronic devices with wireless communication modules, including steps 201-203:

[0044] Step 201: Obtain historical detection information obtained by the wireless communication module from detecting target objects in the network detection environment within a historical time period.

[0045] Step 202: Predict the future appearance time of the target object within the network detection environment based on the historical detection information.

[0046] Step 203: In response to the current time being a first preset time before the future occurrence time, switch the operating mode of the wireless communication module.

[0047] Based on historical detection information, this solution predicts the future appearance time of the target object in the network detection environment and switches the operating mode of the dormant wireless communication module to normal operation mode in advance for a first preset time.

[0048] This demonstrates that the system switches the operating mode of the wireless communication module only when necessary, keeping it in sleep mode the rest of the time, significantly reducing energy consumption. Furthermore, the timing of mode switching is predicted based on historical data, effectively matching the expected appearance time of the target object and greatly reducing invalid wake-ups.

[0049] A network detection environment can be understood as the effective signal range within which an electronic device can detect objects in its vicinity. A network device fingerprint can be understood as the signals detected by an electronic device within its network detection environment, which can be used to identify the network device. This signal can be actively emitted by the network device and intercepted by the electronic device; or it can be actively emitted by the electronic device and returned after detecting a target object.

[0050] In one embodiment, historical detection information may consist solely of network device fingerprints of network devices within the network detection environment, or solely of biometric behavioral information, or a combination of both. The biometric behavioral information may include movement speed, direction of movement, stride length, stride frequency, movement range, and duration of stay in different spatial areas.

[0051] In one embodiment, when predicting the future occurrence time of a target object each time, the prediction can be made based on all historical detection information from the past.

[0052] Predictions can also be made based on historical probe data within a sliding time window prior to the current moment. For example, predictions can be made based on historical probe data from the week most recent. By using the most up-to-date historical probe data, interference from outdated and obsolete information can be avoided.

[0053] In one embodiment, after the wireless communication module detects a target object in the network detection environment and obtains detection information, it can associate and store the detection information with the timestamp of the time the detection information was obtained.

[0054] When predicting the future appearance time of a target object in a network detection environment based on historical detection information, the detection information and timestamp can be input into the time prediction model. The time prediction model predicts the next future appearance time of the target object by analyzing the temporal regularity of the detection information of different contents on the timeline.

[0055] For example, historical detection information includes detection information 1 that occurred at time A, detection information 2 that occurred at time B, and detection information 3 that occurred at time C. The time prediction model predicts the time when new detection information will appear next by analyzing the temporal correlation of different detection information, that is, the future appearance time of the target object.

[0056] The operating mode of the wireless communication module switches at a time when the current time is a first preset duration before the future occurrence time. For example, assuming the predicted future occurrence time of the target object is 10:00 and the first preset duration is 5 minutes, the operating mode of the wireless communication module can be switched at 9:55. By reserving a safety margin for the prediction result, the impact of prediction errors can be reduced.

[0057] Of course, if the first preset duration is 0, then the operating mode of the wireless communication module will switch from the current moment to the future moment.

[0058] In one embodiment, besides determining the timing for switching the operating mode of the wireless communication module based on predicting the future appearance time of the target object within the network detection environment using historical detection information, the timing for switching the wireless communication module's mode can also be confirmed in response to a linkage control command sent by a linkage device linked to the electronic device. The operating mode of the wireless communication module is switched in response to a second preset time elapsed before the future appearance time determined based on the switching mode's timing.

[0059] For example, in the scenario of home door monitoring, the linked devices can be smart elevators, smart lights, etc. in the public area where the electronic device is located.

[0060] For example, when the linked device is a smart elevator, the timing for the wireless communication module to switch modes is determined in response to a linkage control command sent by the smart elevator linked to the electronic device. For instance, the smart elevator sends a linkage control command to the electronic device in response to the button on the target floor being pressed, indicating that it is about to arrive at the target floor. Alternatively, the smart elevator may send a linkage control command to the electronic device upon arrival at the target floor, indicating that it has arrived at the target floor.

[0061] For example, when the linked device is a smart light, the smart light can be located in a public area where the electronic device is located. When the smart light is turned on, it indicates that a person is present in that public area. In response to a linkage control command sent by the smart light linked to the electronic device, the timing of the wireless communication module switching mode is confirmed. For instance, when the smart light is turned on, indicating that a person is present in the public area where the smart light is located, the smart light can send a linkage control command to the electronic device to indicate that a person may soon appear around it.

[0062] In this embodiment, for example, if the smart elevator reaches a floor, the target object is highly likely to appear on that floor; if the smart light turns on, the target object is highly likely to appear near the electronic device. By linking with IoT devices around the electronic device, the accuracy of the predicted switching timing can be ensured. Furthermore, the entire process does not involve the prediction process of an artificial intelligence model; it only determines the switching timing based on the linkage control command, which can also adapt to the problem of insufficient processing power of some electronic devices.

[0063] In one embodiment, after switching the operating mode of the wireless communication module, the wireless communication module can also obtain the fingerprint of the network device in the current network detection environment, determine the identity of the corresponding target object based on the network device fingerprint, or combine it with biometric behavioral information to determine the identity of the target object, and switch the operating mode of the electronic device based on the identity of the target object.

[0064] In one embodiment, the target object may be a living being, which may be, for example, a person holding a network device, or the network device itself.

[0065] If the target is a person who owns a network device, then the network device identity corresponds to the person's identity. After identifying the device identity based on the network device's fingerprint, the person's identity can be determined based on the device identity. For example, in an exemplary surveillance scenario at a resident's door, assuming that the network device is connected to the resident's electronic device based on the network device's fingerprint, it can be determined that the device belongs to the resident, and therefore the person owning the device is the resident.

[0066] If the target is the device itself, the network device's identity can be directly determined. For example, taking a neighbor's door lock as an example, in a typical home security monitoring scenario, if the electronic device detects that the Bluetooth signal strength of a network device in the monitoring environment remains consistently stable within a fixed range and the duration exceeds a preset threshold, then based on this information, the network device can be identified as the neighbor's door lock. Before the neighbor appears within the monitoring range, the neighbor's door lock will automatically connect to the neighbor's portable device or emit a sound signal in advance. Based on changes in the neighbor's door lock's connection status or sound signals—for example, if the neighbor's door lock switches from a continuously disconnected state to a connected state, or if the door lock emits a "welcome home" sound that is captured by the electronic device—it can be further determined that the neighbor is about to appear within the monitoring range, and the electronic device's operating mode can be switched to the mode corresponding to the neighbor's identity.

[0067] In one embodiment, if the identified target is a biological entity, the target's identity can be determined solely based on the network device fingerprint, solely based on biological behavioral information, or a combination of both. For example, the device identity of the network device carried by the target person can be determined based on the network device fingerprint, and the target person's identity can be determined by combining the biological behavioral information. If the identified target is a network device, the network device's identity can be determined based on its fingerprint.

[0068] For example, in scenarios where network devices are separated from people, such as a neighbor's door lock not being attached to the neighbor, the network device's identity can be determined solely based on its fingerprint. Alternatively, the identity of a creature can be determined by directly detecting its biometric information within a network detection environment. Or, when a creature and a network device coexist, such as a person possessing a network device, the target's identity can be determined by combining the network device's fingerprint and biometric information.

[0069] In an exemplary surveillance scenario, such as a surveillance system monitoring a resident's doorstep, the operating mode of the electronic device can be switched based on the identity of the target object. For example, if it is not desired that the surveillance device at the resident's doorstep collect the biometric information of a neighbor, the current regular monitoring mode of the electronic device can be switched to a privacy protection mode when the target object about to appear in the monitoring range is identified as a neighbor. Specifically, the privacy protection mode can include turning off the biometric information collection module, adjusting the angle of the biometric information collection module to avoid pointing it at the specific target object, reducing the resolution of the biometric information collection module, or blurring the images collected by the biometric information collection module.

[0070] In existing technologies, to address privacy protection needs, the IDs of network devices requiring privacy protection are stored in a whitelist. Typically, network devices and monitoring devices are located on the same local area network (LAN). The monitoring device can obtain the ID of the network device through the LAN connection and, upon determining that the ID of the network device appears in the whitelist, switches to privacy protection mode.

[0071] However, the above method only works for internal network devices and cannot identify non-internal network devices that have not established a connection with the monitoring device. Of course, for network security reasons, monitoring devices or the local area network they reside on also do not want to establish any connections with untrusted devices.

[0072] It's easy to understand that existing solutions fail when identifying devices outside the internal network. For example, existing technologies cannot be used to identify neighboring devices.

[0073] In one embodiment, the network device fingerprint in the network detection environment can be obtained in a disconnected state, where the electronic device and the network device have not established any effective communication link.

[0074] In this embodiment, obtaining the fingerprint of the network device in a connectionless manner can avoid the risk of data leakage of the electronic device itself, and can also determine the identity of the target object based on the obtained network device fingerprint without the need for authorization from others.

[0075] In one embodiment, the disconnected method can be to detect micro-motion features (such as breathing rate, limb movement, etc.) in the environment through a radar module detection network.

[0076] Non-connectivity methods can also be achieved through the Bluetooth Low Energy (BLE) sniffing function of Bluetooth devices, used to capture data packets transmitted by network devices using BLE technology within a network probing environment. For example, it can capture device identifiers (Hashed MAC), signal strength (RSSI, Received Signal Strength Indicator), service UUID (Universally Unique Identifier) ​​(such as 0xFDEE, an Apple proprietary service), and transmit power from broadcast packets. Random MAC tracking technology is employed: an RF fingerprint is established for the broadcast packet length and carrier frequency offset (CFO) of the same device (e.g., Apple device CFO: 5.2kHz ± 0.1kHz).

[0077] Non-connectivity methods can be achieved by analyzing nearby Wi-Fi signals using the deep analysis function of the Wi-Fi system. For example, extracting the SSID (Service Set Identifier) ​​(such as "Starbucks"), Vendor OUI (Organizationally Unique Identifier) ​​(such as Apple's 00:17:F2), and supported speeds (such as 802.11ax, mostly for mobile phones) from the Probe Request frame.

[0078] In a non-connectivity mode, the location of network devices can also be determined by measuring the distance to the network devices through UWB (Ultra-Wideband) positioning function.

[0079] The above methods allow for the acquisition of network device fingerprints in a connectionless manner, even when no effective communication link is established between the electronic device and the network device. It should be noted that one or more network device fingerprints can be acquired using one or more of the above methods.

[0080] In one embodiment, when acquiring network device fingerprints within a network probing environment, these fingerprints can be acquired based on multiple non-established states. When acquiring network device fingerprints based on multiple non-connected states, a first network device fingerprint can be acquired using a low-power wireless communication module in a non-connected state. Based on this first fingerprint, it can be determined whether a high-precision wireless communication module needs to be activated. If necessary, the high-precision wireless communication module can be activated, and a second network device fingerprint detected by the high-precision wireless communication module can be acquired. The identity of the target object is then determined based on the first and second network device fingerprints. The low-power wireless communication module may include radar and Bluetooth devices; the high-precision wireless communication module may include UWB positioning devices and Wi-Fi systems.

[0081] For example, if the behavior information of an object is determined based on the fingerprint of a first network device, and the behavior information indicates that the object will appear, then it is determined that a high-precision wireless communication module needs to be activated. For example, in an exemplary home doorway monitoring scenario, if a person holding a network device riding an elevator, or another person holding a network device appearing near the stairwell but not walking towards the monitoring range, is detected by a low-power wireless communication module, and it is determined based on the acquired network device fingerprint that the person will not appear in the monitoring range, then the high-precision wireless communication module does not need to be activated. For example, assuming the low-power wireless communication module is Bluetooth, the judgment could be based on the gradual increase in Bluetooth signal strength, which could indicate that someone is approaching nearby, and the high-precision wireless communication module could be activated to further identify the person's identity; if the signal slightly increases and then quickly weakens, it could indicate that the person is passing by the vicinity of the monitoring range, and in this case, the high-precision wireless communication module does not need to be activated to further identify the person's identity.

[0082] In this embodiment, objects in the network environment are first detected using a low-power wireless communication module. Only when it is determined that the object requires identification is a high-precision wireless communication module activated, working in conjunction with the low-power module to acquire the network device's fingerprint. This mechanism offers dual advantages: 1. Significantly reduced energy consumption: the high-precision module is only activated when necessary, avoiding ineffective operation; 2. Improved identification accuracy: by fusing multi-mode detection results, the identity of the target object is identified more accurately.

[0083] In one embodiment, the identity of the network device is identified and the identity of the target object is determined based on the network device fingerprint; or the identity of the target object is determined by combining biometric behavioral information.

[0084] When identifying a network device based on its fingerprint, at least one feature index value of at least one dimension of device features can be extracted from the network device fingerprint, and the device identity of the corresponding network device can be determined based on the at least one feature index value.

[0085] The network device fingerprint can include physical layer information, protocol layer information, and behavioral layer information of the network device. For example, the dimensions of the device characteristics can be divided into physical dimension, protocol dimension, and behavioral dimension, wherein: the physical dimension's physical characteristics feature indicators include carrier frequency offset, modulation error, and signal strength fluctuation standard deviation; the protocol dimension's protocol characteristics feature indicators include BLE service and Wi-Fi probes; and the behavioral dimension's behavioral characteristics feature indicators include time-of-day activity, dwell time, and movement trajectory.

[0086] There are no restrictions on the extraction methods for carrier frequency offset, modulation error, and standard deviation of signal strength fluctuation. For example, BLE service can extract UUID, and Wi-Fi probe can extract WiFi channel width or OUI.

[0087] Activity level during a given time period, dwell time, and movement trajectory are indicators that can be comprehensively calculated based on the detection results and used to analyze the behavioral information of the target object.

[0088] For example, time-based activity can be calculated using -Σ(p*log2(p)) to determine the active interval of a target object within 24 hours, thus capturing patterns in the target object's appearance time. Here, P represents the probability of appearance within a specific time interval. A lower time-based activity level indicates a more consistent time period for the target object's appearance.

[0089] For example, the coefficient of variation of dwell time can be expressed as the ratio of the standard deviation to the mean of the dwell time for the same network device over a week. This metric can be used to analyze the regularity of dwell time for a target object.

[0090] For example, the smoothness of a movement trajectory can be measured by the ratio of the straight-line distance to the actual path length of the target object. A ratio closer to 1 indicates that the movement path of the target object is closer to a straight line, while a ratio closer to 0 indicates that the movement path of the target object is more circuitous.

[0091] In one embodiment, when determining the device identity of a corresponding network device based on feature index values, at least one feature index value can be fused to obtain a first fused index value for the corresponding dimension, and the device identity of the corresponding network device can be determined based on the first fused index value for the corresponding dimension.

[0092] For example, if the device features are divided into a dimension, and there is only one feature index value under that dimension, the device identity of the corresponding network device can be directly determined based on the feature index value. In this case, the fusion process assigns a weight of 1 to the feature index value.

[0093] For example, if the device features are divided into a dimension and there are multiple feature index values ​​under that dimension, the multiple feature index values ​​can be fused to obtain a first fused index value, and the device identity of the corresponding network device can be determined based on the first fused index value.

[0094] For example, if the device features are divided into multiple dimensions, and each dimension has at least one feature index value, then at least some feature index values ​​of at least some dimensions can be selected for fusion processing to obtain the first fusion index value of the corresponding dimension, and the device identity of the corresponding network device can be determined based on the first fusion index value of the corresponding dimension.

[0095] For example, dimension A includes a1, a2, and a3; dimension B includes b1 and b2; and dimension C includes c1, c2, and c3. The first fusion index value for dimension A can be determined based on a1 and a2; the first fusion index value for dimension B can be determined based on b1 and b2; and the first fusion index value for dimension C can be determined based on c1, c2, and c3. The device identity of the corresponding network device can then be determined based on the first fusion index values ​​for each dimension.

[0096] In one embodiment, when determining the device identity of a corresponding network device based on a first fusion index value of a corresponding dimension, the current environment scenario category can be determined, and a first weight of each first fusion index value corresponding to the current environment scenario category can be obtained, or the first weight of each first fusion index value can be adaptively determined. The first fusion index values ​​are then fused according to the first weights to obtain a second fusion index value, and the device identity of the corresponding network device is determined based on the second fusion index value.

[0097] For example, environmental scenarios can be categorized into nighttime mode, strong interference environment mode, and long-term monitoring mode. For nighttime mode, the physical layer weight is greater than the behavioral layer weight, which is greater than the protocol layer weight. For strong interference environment mode, the physical layer weight is greater than or equal to the protocol layer weight, which is greater than the behavioral layer weight. For long-term monitoring mode, the physical layer weight is less than the protocol layer weight, which is less than the behavioral layer weight. The environmental scenario category can be set by the user on the terminal. The electronic device can then use the weights corresponding to the user-defined environmental scenario category to fuse the first fusion index values ​​of each dimension to obtain the second fusion index value.

[0098] In one embodiment, confidence levels can also be used for weighting, as shown in the following formula:

[0099]

[0100] in, This is the second fusion index 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 layer dimension.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] Specifically, such as Figure 3 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.

[0106] Fusing multiple feature values ​​from any dimension yields a first fusion index value. Any single fusion index value can be used independently for identity recognition. For example, a first fusion index value of 0.45 for the physical dimension can independently identify the target object as a neighbor. Similarly, a first fusion index value of 0.45 for the protocol dimension can also independently identify the target object as a neighbor. During fusion processing, multiple feature values ​​can be weighted, assigning higher weights to more important features and lower weights to less important features.

[0107] Multiple first fusion index values ​​can be fused to obtain a second fusion index value. This second fusion index value can be used as the identification result for identity verification. For example, a second fusion index value of 0.45 can identify the target object as a neighbor. During the fusion process, multiple first fusion index values ​​can be weighted, assigning higher weights to more important first fusion index values ​​and lower weights to less important ones.

[0108] In one embodiment, for identification results determined at adjacent moments within a continuous time period, if the difference between the identification result at the current moment and the identification result at the previous moment is greater than a preset threshold, then according to a time decay strategy, the identification result at the current moment is adjusted using the identification result at the previous moment. The identity of the target object at the current moment is determined based on the adjusted identification result.

[0109] The recognition result at the current time step can be adjusted using the recognition result from the previous time step using the following formula:

[0110]

[0111] in, For a moment The recognition results For a moment The recognition results for and The difference between them.

[0112] For example, if the fingerprint of a network device is obtained based on multiple wireless communication modules, and there is a conflict between the identification results of different wireless communication modules, time decay arbitration can be introduced to prevent erroneous decisions caused by the conflict.

[0113] For example, as shown in Table 1:

[0114] Table 1

[0115]

[0116] In Table 1, at time t=0, the decision result based on BLE is in the pending confirmation state, and the target object's identity is not directly determined based on the current device fingerprint. At time t=2, continuous monitoring is possible. When making decisions based on UWB, the weight of the previous BLE decision results in the UWB decision is reduced over time. Through continuous adjustment, conflicts between them can be prevented. At time t=5, the response terminates and monitoring stops.

[0117] In one embodiment, after acquiring the fingerprint of a network device within the network detection environment, the current connection status between the network device and the electronic device can be determined based on the network device fingerprint. When determining the device identity of the corresponding network device based on the network device fingerprint, in response to the current connection status being connected, the network device is determined to be a first type of device; in response to the current connection status being disconnected, the network device is determined to be a second type of device.

[0118] In an exemplary home door monitoring scenario, the first type of device is the resident's device, and the second type of device is other people's devices.

[0119] In this embodiment, based on the assumption that only residents will establish connections with electronic devices, the device that has established a connection with the electronic device can be accurately identified as the resident's device based on the current connection status between the network device and the electronic device.

[0120] In one embodiment, after responding to the current connection status being disconnected, the historical activity of the network device can be determined based on the network device fingerprint. If the historical activity is greater than a preset threshold, the network device is identified as a third type of device, wherein the third type of device is a neighboring device.

[0121] Combining the previous two embodiments, the connection status distinguishes resident devices from other people's devices, and historical activity can further identify neighboring devices from other people's devices. This allows for the identification of resident devices and neighboring devices without establishing a connection with any network device.

[0122] Historical activity can be measured using characteristic indicators such as time-period activity and dwell time coefficient of variation, as described in the aforementioned embodiments. If time-period activity is used, historical activity is the reciprocal of time-period activity. For example, if time-period activity is less than 1.5, it can be identified as a neighbor's device. If dwell time coefficient of variation is used, historical activity can be the reciprocal of dwell time coefficient of variation. For example, if dwell time coefficient of variation is less than 0.1, it can be identified as a neighbor's device.

[0123] Of course, this solution does not limit the identification of other objects based on network device fingerprints. For example, the identity of service personnel can also be determined based on the UUID in the network device fingerprint.

[0124] In one embodiment, the fingerprints of network devices appearing in the network detection environment within a preset time period can be obtained, the device identity of the corresponding network device can be determined based on the network device fingerprint, a management list with 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.

[0125] For example, obtaining the fingerprint of a network device appearing in the network detection environment within a preset time period can be either the network device fingerprint currently obtained in real time or the network device fingerprint of the same network device within a historical time period.

[0126] In one embodiment, a network device fingerprint within the network probing environment is acquired. If the network device corresponding to the fingerprint is in any managed list, the target object's identity is determined to be the identity corresponding to that managed list. If the network device corresponding to the fingerprint is not in any managed list, the target object's identity is determined based on the network device fingerprint.

[0127] In this embodiment, different management lists correspond to the identities of different objects. After determining the device identity of a network device, the network device can be added to the corresponding management list. When a network device reappears in the management list, the identity of the target object can be determined based on the management list, without needing to determine the target object's identity again based on the network device fingerprint. This improves the efficiency of identifying recurring target objects and reduces the error associated with determining the target object's identity based on the network device fingerprint.

[0128] In one embodiment, when adding a network device to the corresponding management list, a network device fingerprint that meets the first preset condition and is used to identify the network device can be added to the first list; a network device fingerprint that meets the second preset condition and is used to identify the network device can be added to the second list.

[0129] For example, a network device fingerprint can uniquely identify a network device. By matching the network device fingerprint obtained in the network detection environment with the network device fingerprint stored in the management list, it can be determined whether the network device appears in the management list.

[0130] For example, the first list can be a permanent whitelist used to store the network device fingerprints of residents' network devices; the second list can be a neighbor list used to store the network device fingerprints of neighbors' network devices.

[0131] The first preset condition could be that a network device establishes a connection with an electronic device, or that a network device establishes a connection with an electronic device and the trajectory smoothness is greater than a preset value, or that a network device establishes a connection with an electronic device and detects a door opening action within a preset time period. For example, if a network device establishes a connection with an electronic device, it indicates that the network device belongs to the resident. As another example, if a network device simultaneously establishes a connection with an electronic device and the trajectory smoothness is greater than a preset threshold, it indicates that the network device is moving in a straight line towards the electronic device. Similarly, if a network device simultaneously establishes a connection with an electronic device and detects a door opening action within a preset time period, it can double-verify that the connected network device belongs to the resident.

[0132] A heatmap is generated based on the network device's fingerprint. This heatmap characterizes the temporal and / or regional aggregation features of the corresponding network device's appearance. A second preset condition may be that the network device has not established a connection with any electronic device and appears n times within a fixed time period. The fixed time period can be represented using a heatmap; by measuring the similarity of the heatmaps showing n appearances, it can be determined whether the network device appears n times within a fixed time period and region. Alternatively, the timestamps of the network device's appearance can be recorded, only determining if the network device appears n times within a fixed time period.

[0133] In one embodiment, the user can also manually add network devices to the management list. In response to a network device add command, which includes the network device fingerprint, the network device is added to the corresponding management list.

[0134] Users can identify the devices; for example, users can add family members' devices to the corresponding management list.

[0135] The user does not know the identity of the device. For example, the user can search for Bluetooth signals from non-family members in the vicinity and input the network device fingerprint of the network device emitting the Bluetooth signal into the electronic device. The electronic device then determines the corresponding management list based on this and adds the network device to the corresponding management list.

[0136] In one embodiment, the management list may further include an observation list. When adding a network device to the corresponding management list, a network device fingerprint that meets a third preset condition and is used to identify the network device may be added to the observation list.

[0137] For example, the watchlist could be a list of service personnel, such as delivery drivers. The third preset condition could be that the network device fingerprint contains a UUID, and that UUID is unique to the service personnel. Of course, the watchlist could also be a list of other personnel, and the third preset condition could be adjusted to correspond to the identification of other personnel.

[0138] In one embodiment, for each network device in the watchlist, the network device is removed from the management list if it meets a fourth preset condition.

[0139] For example, the fourth preset condition could be that it does not occur a second time within a preset future period.

[0140] In one embodiment, the management list also includes a temporary list. When adding a network device to the corresponding management list, the network device can be added to the temporary list and the frequency of the network device's appearance can be updated. If the frequency of appearance is greater than the frequency threshold, the network device is added to the management list corresponding to the personnel's identity.

[0141] In this embodiment, by determining the frequency of occurrence of network devices in the temporary list, the addition of accidentally appearing or misidentified network devices to the management list is prevented, thereby improving the accuracy of the network device identities in the management list.

[0142] In one embodiment, different operating modes are executed based on different management lists.

[0143] For example, in an exemplary home door monitoring scenario, the management list may include a neighbor list and a resident list. In practical applications, the operating mode may include a normal mode and a privacy protection mode. If a network device from the resident list is detected at the home door, the privacy protection mode is applied to both indoor and outdoor electronic devices. If a network device from the neighbor list is detected at the home door, the privacy protection mode is applied to outdoor electronic devices. In other cases, the operating mode can be normal mode. The privacy protection mode may include disabling the biometric data acquisition module, adjusting the angle of the biometric data acquisition module to avoid aligning it with the target object, reducing the resolution of the biometric data acquisition module, or blurring the images acquired by the biometric data acquisition module.

[0144] Corresponding to the embodiments of the foregoing methods, this specification also provides embodiments of the apparatus and the terminal to which it is applied.

[0145] Figure 4 This is a schematic diagram illustrating the structure of an electronic device according to an exemplary embodiment. Figure 4As shown, at the hardware level, the electronic device 400 includes a processor 402, an internal bus 404, a network interface 406, memory 408, and non-volatile memory 410, and may also include other hardware required for business operations. One or more embodiments of this specification can be implemented in software, for example, the processor 402 reads the corresponding computer program from the non-volatile memory 410 into memory 408 and then runs it. Of course, in addition to software implementation, one or more embodiments of this specification do not exclude other implementation methods, such as logic devices or a combination of hardware and software, etc. That is to say, the execution subject of the following processing flow is not limited to each logic module, but can also be hardware or logic devices.

[0146] Figure 5 This is a block diagram illustrating an operating apparatus for a wireless communication module according to an exemplary embodiment. Figure 5 As shown, this device can be applied to, for example Figure 4 The electronic device 400 shown implements the technical solution of this specification. The device includes:

[0147] The acquisition unit 502 is used to acquire historical detection information obtained by the wireless communication module from detecting target objects in the network detection environment within a historical time period.

[0148] The prediction unit 504 is used to predict the future appearance time of the target object in the network detection environment based on the historical detection information.

[0149] Control unit 506 is used to switch the operating mode of the wireless communication module in response to a time period of a first preset duration before the future occurrence time.

[0150] Optionally, the target object includes network devices and / or biological entities, and the historical detection information includes at least one of the following: network device fingerprints and biological behavioral information within the network detection environment; the network device fingerprint includes the physical layer information, protocol layer information, and behavioral layer information of the network device.

[0151] Optionally, the network device fingerprint in the network detection environment is obtained in a disconnected state; the disconnected state means that the electronic device and the network device have not established any effective communication link.

[0152] Optionally, the prediction unit 504 is further configured to confirm the time of the wireless communication module switching mode in response to a linkage control command sent by a linkage device linked with the electronic device. The control unit 506 is further configured to switch the operating mode of the wireless communication module in response to a time period before the future occurrence time determined based on the time of the switching mode.

[0153] Optionally, the device further includes a device fingerprint acquisition module for acquiring network device fingerprints within the current network detection environment; an object identity determination module for determining the identity of a corresponding target object based on the network device fingerprint, or by combining biometric behavioral information to determine the identity of the target object; and an operating mode switching module for switching the operating mode of the electronic device based on the identity of the target object.

[0154] The specific implementation process of the functions and roles of each module in the above device can be found in the implementation process of the corresponding steps in the above method, and will not be repeated here.

[0155] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of the solution in this specification according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0156] This specification also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the operation method of any of the aforementioned wireless communication modules provided in this application.

[0157] Specifically, computer-readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media, and memory devices, such as 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.

[0158] This specification also provides a computer program product, including a computer program / instructions, which, when executed by a processor, implement the steps of the operation method of any of the aforementioned wireless communication modules.

Claims

1. A method for operating a wireless communication module, applied to an electronic device with a wireless communication module, characterized in that, The method comprises: obtaining historical detection information of a target object in a network detection environment detected by a wireless communication module in a historical time period; the historical detection information comprises at least one of network device fingerprints in the network detection environment and biological behavior information; wherein the network device fingerprints comprise physical layer information, protocol layer information and behavior layer information of the network device; the network device fingerprints in the network detection environment are obtained in a non-connected state; the non-connected state is that the electronic device has not established any valid communication link with the network device; predicting a future occurrence time of the target object in the network detection environment based on the historical detection information; switching a running mode of the wireless communication module in response to a current time being a time before the future occurrence time by a first preset time length; obtaining network device fingerprints in a current network detection environment; determining an identity of the corresponding target object based on the network device fingerprints; and switching the running mode of the electronic device based on the identity of the target object.

2. The method of claim 1, wherein, The method further comprises: confirming a time when the wireless communication module switches the mode in response to a linkage control instruction sent by a linkage device linked with the electronic device; switching the running mode of the wireless communication module in response to a current time being a time before a future occurrence time determined based on the time when the mode is switched by a second preset time length.

3. The method of claim 1, wherein, The determining of the identity of the target object based on the network device fingerprints comprises identifying the identity of the network device based on the network device fingerprints and determining the identity of the target object; or, determining the identity of the target object based on the network device fingerprints and in combination with the biological behavior information.

4. An operating device of a wireless communication module, characterized by comprising: The apparatus comprises: an obtaining unit, configured to obtain historical detection information of a target object in a network detection environment detected by a wireless communication module in a historical time period; the historical detection information comprises at least one of network device fingerprints in the network detection environment and biological behavior information; wherein the network device fingerprints comprise physical layer information, protocol layer information and behavior layer information of the network device; the network device fingerprints in the network detection environment are obtained in a non-connected state; the non-connected state is that the electronic device has not established any valid communication link with the network device; a predicting unit, configured to predict a future occurrence time of the target object in the network detection environment based on the historical detection information; a control unit, configured to switch a running mode of the wireless communication module in response to a current time being a time before the future occurrence time by a first preset time length; a device fingerprint obtaining unit, configured to obtain network device fingerprints in a current network detection environment; a running mode switching unit, configured to determine an identity of the corresponding target object based on the network device fingerprints; and switch the running mode of the electronic device based on the identity of the target object.

5. An electronic device comprising a wireless communication module, a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor implements the steps of the method of any one of claims 1-3 when executing the program.

6. 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 of any one of claims 1-3.

7. A computer program product, characterised in that, The computer program / instruction is executed by the processor to implement the steps of the method of any one of claims 1-3.

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