An adaptive adjustment method and apparatus

Abstract

The application provides a self-adaptive adjustment method and device, which uses a method of self-adaptive adjustment of a trigger threshold without increasing hardware, and realizes a low-cost and high-efficiency intelligent lock for coping with complex environments.

Description

Technical Field

[0001] This application relates to the field of wireless terminals, and more particularly to an adaptive adjustment method and apparatus. Background Technology

[0002] With the development of communication technology, smart devices have become ubiquitous in people's lives. For example, smart locks can use wireless signals to intelligently identify and automatically open, making daily life more convenient. However, smart locks are not accurate enough when facing more complex environments.

[0003] To make smart locks smarter and more energy-efficient, improving their recognition functions has become an urgent problem to be solved. Summary of the Invention

[0004] This application provides an adaptive adjustment method and apparatus. By using an adaptive adjustment method combined with the judgment of moving human body characteristics, the detection results can be made more accurate and efficient without the need for additional hardware.

[0005] To achieve the above objectives, this application adopts the following technical solution:

[0006] In a first aspect, this application provides an adaptive adjustment method, comprising: determining whether a target-related first feature is met based on first data; when the determination result based on the first data does not meet the first feature, determining whether a first condition is triggered based on second data, wherein the second data is data collected after the first data; when the determination result based on the second data meets the first condition and the number of times the first condition is triggered exceeds a first threshold, adjusting a first threshold associated with the segment range to a second threshold based on the segment range where the target is located, wherein both the first threshold and the second threshold are related to triggering the first condition, and the second threshold is more difficult to trigger the first condition than the first threshold.

[0007] Based on the method provided in the first aspect above, after initially determining that the target does not conform to human movement characteristics, further analysis is conducted to determine whether this determination is a false trigger. If the number of triggers meeting the conditions exceeds a preset threshold, it can be determined as a false trigger. In this case, to avoid more false triggers in similar situations later, the threshold is adjusted to make the conditions for false triggering more difficult. This method can reduce false triggers in the current environment, thereby saving power and improving efficiency.

[0008] In one possible implementation, after adjusting the first threshold to the second threshold, the method further includes:

[0009] In the first time, based on the third data, it is determined whether the first condition is met. After the third data is the second data, the data is collected in the first time.

[0010] When the judgment based on the third data meets the first condition, the second threshold is adjusted to the third threshold. The third threshold is related to the first condition, and the third threshold is more difficult to trigger the first condition than the second threshold.

[0011] When the judgment based on the third data does not meet the triggering condition for the first condition, the second threshold is adjusted to the fourth threshold. The fourth threshold is related to the first condition and is more likely to trigger the first condition than the second threshold. The fourth threshold is a value within the range of the first and second thresholds.

[0012] Based on the above method, an adaptive adjustment approach is used to continuously monitor for false triggering after adjusting the false trigger threshold for a period of time. If false triggering still occurs, the threshold is further adjusted to make false triggering even less likely. Conversely, if no false triggering occurs within a certain period, it indicates that the threshold adjustment may have been too lenient, and the threshold is adjusted back. This method ensures that the door lock maintains the most suitable monitoring capability under the current environment.

[0013] In one possible implementation, the method further includes:

[0014] When the judgment result based on the first data matches the first feature, the corresponding business is executed based on the segment range where the target is located.

[0015] Among them, the segment range is related to the distance and belongs to a preset set of more than one segment range. Based on the first distance, the segment range where the target is located is determined.

[0016] In one possible implementation, performing business functions includes:

[0017] When the target is within the nearest segment, perform face unlock;

[0018] Perform a pause capture when the target is in a relatively distant second segment range, or when face unlock fails.

[0019] In one possible implementation, the first feature is the human movement feature.

[0020] In one possible implementation, the first data, the second data, and the third data are sensor echo data of three frames or more, and the second data and the third data are greater than or equal to the first data.

[0021] In one possible implementation, if the judgment result based on the second data does not meet the triggering condition, no action is taken, and the system waits for the next trigger to determine whether it meets the first feature.

[0022] In one possible implementation, adjusting the second threshold to a fourth threshold includes:

[0023] The fourth threshold is adjusted based on its percentage relationship with the second threshold.

[0024] In one possible implementation, determining whether a target meets the first characteristic based on target-related first data includes triggering the determination after a moving target is detected by a sensor.

[0025] In one possible implementation, the sensor includes sensors used for human detection, such as millimeter-wave radar, ultrasonic radar, or infrared ranging sensors.

[0026] Secondly, an electronic device is provided for implementing the method provided in the first aspect. The electronic device includes modules, units, or means corresponding to the implementation of the method, which can be implemented in hardware, software, or by hardware executing corresponding software. The hardware or software includes one or more modules or units corresponding to the functions described above.

[0027] In one possible implementation, the electronic device may include a processing module and a transceiver module. The processing module can be used to implement the processing functions described in the first aspect and any possible implementation thereof. The processing module may be, for example, a processor. The transceiver module, also referred to as a transceiver unit, is used to implement the sending and / or receiving functions described in the first aspect and any possible implementation thereof. The interface module may consist of a sensor, a transceiver, or a transceiver unit.

[0028] In one possible implementation, the transceiver module is used to collect target-related first data and second data; the processing module is used to determine whether the target meets the first feature based on the first data; determine whether the target meets the triggering condition based on the second data; and adjust the first threshold associated with the segment range to the second threshold.

[0029] In one possible implementation, after adjusting the first threshold to the second threshold, the method further includes:

[0030] In the first time, based on the third data, it is determined whether the first condition is met. After the third data is the second data, the data is collected in the first time.

[0031] When the judgment based on the third data meets the first condition, the second threshold is adjusted to the third threshold. The third threshold is related to the first condition, and the third threshold is more difficult to trigger the first condition than the second threshold.

[0032] When the judgment based on the third data does not meet the triggering condition for the first condition, the second threshold is adjusted to the fourth threshold. The fourth threshold is related to the first condition and is more likely to trigger the first condition than the second threshold. The fourth threshold is a value within the range of the first and second thresholds.

[0033] Based on the above method, an adaptive adjustment approach is used to continuously monitor for false triggering after adjusting the false trigger threshold for a period of time. If false triggering still occurs, the threshold is further adjusted to make false triggering even less likely. Conversely, if no false triggering occurs within a certain period, it indicates that the threshold adjustment may have been too lenient, and the threshold is adjusted back. This method ensures that the door lock maintains the most suitable monitoring capability under the current environment.

[0034] In one possible implementation, the method further includes:

[0035] When the judgment result based on the first data matches the first feature, the corresponding business is executed based on the segment range where the target is located.

[0036] Among them, the segment range is related to the distance and belongs to a preset set of more than one segment range. Based on the first distance, the segment range where the target is located is determined.

[0037] In one possible implementation, performing business functions includes:

[0038] When the target is within the nearest segment, perform face unlock;

[0039] Perform a pause capture when the target is in a relatively distant second segment range, or when face unlock fails.

[0040] In one possible implementation, the first feature is the human movement feature.

[0041] In one possible implementation, the first data, the second data, and the third data are sensor echo data of three frames or more, and the second data and the third data are greater than or equal to the first data.

[0042] In one possible implementation, if the judgment result based on the second data does not meet the triggering condition, no action is taken, and the system waits for the next trigger to determine whether it meets the first feature.

[0043] In one possible implementation, adjusting the second threshold to a fourth threshold includes:

[0044] The fourth threshold is adjusted based on its percentage relationship with the second threshold.

[0045] In one possible implementation, determining whether a target meets the first characteristic based on target-related first data includes triggering the determination after a moving target is detected by a sensor.

[0046] In one possible implementation, the sensor includes sensors used for human detection, such as millimeter-wave radar, ultrasonic radar, or infrared ranging sensors.

[0047] Thirdly, an electronic device is provided, comprising: a processor; the processor being configured to cause the electronic device to perform the method described in any of the preceding aspects by executing a computer program (or computer-executable instructions) stored in a memory, and / or by means of logic circuitry. Optionally, the number of the processors may be one or more.

[0048] In one possible implementation, the electronic device also includes a memory.

[0049] In one possible implementation, the processor and memory are integrated together; alternatively, the memory is independent of the processor.

[0050] In one possible implementation, the electronic device further includes a communication interface for communicating with other devices, such as sending or receiving data and / or signals. Exemplarily, the communication interface may be a transceiver, circuit, bus, module, or other type of communication interface.

[0051] In one possible implementation, the processor and / or memory also include an artificial intelligence (AI) module for implementing AI-related functions. The AI ​​module can implement AI functions through software, hardware, or a combination of both. For example, the AI ​​module may include a RAN intelligent controller (RIC) module. The AI ​​module could be a near real-time RIC or a non-real-time RIC.

[0052] In one possible implementation, the electronic device is a chip or a chip system. Optionally, when the electronic device is a chip system, it can be composed of chips or may include chips and other discrete components.

[0053] Fourthly, a computer-readable storage medium is provided that stores instructions which, when executed on a computer, enable the computer to perform the methods described in any of the preceding aspects.

[0054] Fifthly, a computer program product containing instructions is provided that, when run on a computer, enables the computer to perform the methods described in any of the preceding aspects.

[0055] A sixth aspect provides a chip device including a processor for invoking a computer program or computer instructions in memory to cause the processor to perform any of the implementations of the first aspect described above.

[0056] Optionally, the processor is coupled to the memory via an interface.

[0057] Optionally, the chip device may also include a memory that stores computer programs or computer instructions.

[0058] The technical effects of any of the possible implementations of aspects two through six can be found in the technical effects of the different possible implementations of aspect one above, and will not be repeated here.

[0059] Understandably, provided that the solutions do not contradict each other, the solutions in the above aspects can be combined. Attached Figure Description

[0060] Figure 1 This application provides an illustration of the application scenario.

[0061] Figure 2 A flowchart illustrating the adaptive adjustment method provided in this application;

[0062] Figure 3 , Figure 4 A schematic diagram of the monitoring segment range provided in this application;

[0063] Figure 5 A schematic diagram of the device provided in this application. Detailed Implementation

[0064] Before introducing the technical solution of this application, the relevant technical terms involved in this application are explained. It is understood that these explanations are intended to make this application easier to understand and should not be regarded as a limitation on the scope of protection claimed in this application.

[0065] A passive infrared sensor (PIR) is a type of human detection sensor that uses pyroelectric infrared sensors to detect changes in infrared radiation. It achieves human detection by detecting changes in infrared radiation caused by human movement.

[0066] In intelligent security systems, smart door locks are an emerging trend. However, the sensing coverage of smart door locks is typically limited to the outdoor environment, which can vary significantly due to weather, human factors, and other factors, leading to false positives and missed detections.

[0067] Based on this, this application provides an adaptive adjustment method and apparatus to reduce the false detection rate in complex environments, reduce power consumption, and improve the working efficiency of the apparatus.

[0068] The embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0069] The method provided in this application can be used in various smart locks, such as home door locks, security door locks, and smart car locks. The following examples illustrate this. Figure 1 The method provided in this application will be described using the example shown. Figure 1 This is merely an illustrative diagram and does not constitute a limitation on the applicable scenarios of the technical solutions provided in this application.

[0070] like Figure 1 The diagram shown is a schematic representation of the application scenario of the device provided in this application. Figure 1 The system includes a door 5, a smart lock 4 on the door 5, and components of the smart lock 4 such as a human detection sensor 1, a main control chip 2, and an execution module 3, as well as a moving person 6. The human detection sensor 1 (hereinafter referred to simply as "sensor") can be a transceiver with detection capabilities, such as a millimeter-wave radar, ultrasonic sensor, or (passive) infrared sensor. The main control chip 2 includes a signal processing unit and an environmental feature analysis module. The execution module 3 includes components for facial recognition and lingering capture. When the person 6 moves into the detection range covered by the human detection sensor 1, the human detection sensor 1 sends a signal wave 7. The signal wave 7 hits the person 6, generating a reflected echo 8, which the human detection sensor 1 receives to form data. This data is processed by the smart lock 4 (main control chip 2 and execution module 3).

[0071] Figure 1 The application scenarios of the device are for illustrative purposes only. Door 5 can also be mobile devices such as smart cars, or fixed facilities with security functions.

[0072] Based on the application scenarios described above, such as Figure 2 As shown, this application provides an adaptive adjustment method. For ease of understanding, a smart lock is used as an example for description. The adaptive adjustment method includes the following steps:

[0073] S201. When a moving target moves into the detection range of the smart lock, the human body detection sensor is triggered.

[0074] When the sensor detects a moving target within its detection range, it reports an interrupt to the device. The smart lock then senses that the sensor has been triggered and prepares to take further action.

[0075] S202. The smart lock determines whether the trigger data matches human movement characteristics.

[0076] The smart lock continuously counts the number of times the human detection sensor is triggered and reads the trigger data collected by the sensor. It then compares the trigger data with a pre-set large model to determine whether the detected moving target matches human movement characteristics.

[0077] In one possible scenario, trigger data refers to the data generated by the sensor receiving the echo of a moving target after the human detection sensor is triggered in S201. This could be data spanning several frames or even longer.

[0078] In one possible scenario, for a rapid response, the trigger data here is collected in a short period of time, such as echo data collected within 3 frames after the sensor is triggered.

[0079] Depending on whether the matching result is determined to be a valid trigger or a false trigger, there are two different handling methods.

[0080] S210. The smart lock determines that the trigger is valid based on the trigger data and executes the corresponding business.

[0081] In the first scenario of the matching result, the smart lock, based on the judgment result of S202, confirms that the collected trigger data is a real moving human body. Based on this, it further determines the distance of the moving human body and invokes and executes the corresponding business functions according to the distance.

[0082] In one possible scenario, when a person moving is located relatively far from the smart lock / door, such as within a range of 3-5 meters, the smart lock will invoke its security functions to capture images of individuals exhibiting potentially risky behaviors such as loitering or tailgating, and report these images to the cloud to alert the homeowner.

[0083] In one possible scenario, when a moving person is relatively close to the smart lock / door, such as within 0.5-1.2 meters, the smart lock will activate the facial recognition module to perform identity verification. This avoids running such high-power components 24 / 7, thereby reducing the smart lock's power consumption and improving its battery life.

[0084] S203. The smart lock determines that the triggering data is invalid and continues to collect more sensor triggering data to determine whether the false triggering conditions are met.

[0085] In the second scenario of the matching result, based on the judgment result of S202, the smart lock confirms that the collected trigger data cannot be determined to be a real moving human body. More sensor trigger data is needed for further judgment.

[0086] In one possible scenario, the smart lock continues to collect more data than in S202. For example, if S202 makes a judgment based on the echoes collected within 3 frames, then it collects 10 or more frames of echo data to ensure a more accurate result, that is, to more accurately determine whether the current sensor trigger is a false trigger.

[0087] In one possible scenario, the false triggering condition is determined by whether the number of times the false triggering threshold is exceeded in the collected triggering data reaches a preset value. For example, if the triggering data is obtained by collecting echoes, the waveform of the echo can be compared with a preset false triggering threshold. Waveforms exceeding the false triggering threshold are counted as one instance. Once the cumulative number reaches a certain level, it is determined that the false triggering condition is met.

[0088] Based on the judgment result of this step regarding whether it is a false trigger, there are two different situations.

[0089] S204. Based on more triggering data, the smart lock determines that the conditions for false triggering are met, and adjusts the trigger threshold corresponding to the segment range where the false triggering target is located.

[0090] If this is the first scenario corresponding to the false trigger judgment result, it indicates that the false trigger was caused by a non-moving human body, such as an animal, a moving vehicle, vibration of doors and windows, swaying plants, or other environmental interference. To avoid more similar false triggers and wasted power in the future, appropriate adjustments need to be made.

[0091] like Figure 3 As shown, within the detection range of a smart lock or a human detection sensor, the entire detection range is divided into one or more segments according to a preset method. These segments can be divided evenly or based on the importance of the areas. For example... Figure 4 As shown, each of these multiple segmented ranges corresponds to a different detection trigger threshold (corresponding to trigger S201), which can also be simply referred to as the trigger threshold. Understandably, the further a segment is from the smart lock, the more difficult it is to trigger. This is because, generally speaking, for the same device, the detection accuracy is usually lower in more distant areas, so setting it to be more difficult to trigger is more reasonable.

[0092] S211. Based on more triggering data, the smart lock determines that the conditions for false triggering are not met this time, and waits for the sensor to be triggered again.

[0093] In one possible scenario, such phenomena may occur due to special circumstances, such as a fast-moving target at the edge of the monitoring range that has already left the monitoring range by the time of subsequent judgment. In such cases, no action is taken, and the process returns to wait for S201 to be executed again.

[0094] S205. After adjusting the trigger threshold, observe whether the human body monitoring sensor is triggered within a specified time.

[0095] This step follows S204 and is part of the adaptive adjustment. After adjusting the trigger threshold corresponding to the target segment range where the movement is located, it continuously observes whether any human body monitoring sensors are triggered within this segment range for a preset time period.

[0096] Based on the observation results, there are two different situations.

[0097] S206. The smart lock did not detect the human body detection sensor being triggered within the specified time, and the trigger threshold was called back.

[0098] Based on the first scenario of the observation results, if no human detection sensor is detected within the segment range of the modified trigger threshold within the preset time period, it's possible that the trigger threshold was adjusted too much, causing normally monitored moving targets to fail to trigger properly due to the adjusted threshold. Therefore, a trigger threshold callback is needed to achieve adaptive recovery capability once the complex environmental factors are eliminated.

[0099] In one possible scenario, both adjustments and callbacks can be made as percentages. For example, in S204, the initial adjustment is 120% of the preset trigger threshold, and then in this step, it is callbacked to 110%.

[0100] In one possible scenario, after a callback, the trigger threshold might be more easily triggered than the originally preset value. Although the previous explanation assumed a false trigger occurred, in reality, the preset trigger threshold might be too high compared to the current environment, causing normal human movement that should be monitored to not be triggered, resulting in no false trigger events. Therefore, to avoid this situation, the smart lock needs to have the ability to adjust the trigger threshold to a value exceeding the preset value.

[0101] According to the second scenario of the observation results, if the human detection sensor is detected to be triggered within the segmented range of the modified trigger threshold within the preset time period, the subsequent processing shall be carried out in accordance with the method of S202 above.

[0102] This cyclical approach helps smart locks maintain a low false detection rate in complex and ever-changing environments.

[0103] The above mainly describes the solution provided in this application from the perspective of the process and method for handling events. Correspondingly, this application also provides an electronic device, which can be a smart lock as described in the above method embodiments, or a device that implements the functions of such a smart lock, or a component that can be used in a smart lock; or, the electronic device can be a terminal as described in the above method embodiments, or a device that includes such a terminal, or a component that can be used in a terminal. It is understood that, in order to achieve the above functions, the smart lock or terminal includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should readily recognize that, in conjunction with the components and algorithm operations disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed by hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0104] This application can divide smart locks or terminals into functional modules based on the above method examples. For example, each function can be divided into its own functional module, or two or more functions can be integrated into one processing module. The integrated modules can be implemented in hardware or as software functional modules. It is understood that the module division in this application is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0105] For example, when dividing the functional modules using an integrated approach. Figure 5 A schematic diagram of an electronic device 500 is shown. The electronic device 500 includes a transceiver module 501 and a processing module 502. The transceiver module 501, also called a transceiver unit, is used to perform transceiver operations; for example, it can be a transceiver circuit, transceiver, or transceiver device. The processing module 502, also called a processing unit, is used to perform operations other than transceiver operations; for example, it can be a processing circuit or a processor.

[0106] In some embodiments, the electronic device 500 may further include a storage module. Figure 5 (Not shown in the image) is used to store program instructions and data.

[0107] In one example, the electronic device is a smart lock, which can be used to implement any of the methods executed by the smart lock in the foregoing embodiments.

[0108] For example, processing module 502 is used to determine whether the data from target-related transceiver module 501 matches human movement characteristics. For example, processing module 502 is used to execute S203 in the aforementioned embodiment, and transceiver module 501 is used to execute S201 in the aforementioned embodiment, which will not be described in detail here.

[0109] In another example, the electronic device is a terminal that can be used to implement any of the methods executed by the terminal in the foregoing embodiments.

[0110] For example, Figure 5 The functions / implementation of the processing module 502 and the transceiver module 501 can be achieved by the processor calling the computer program stored in the memory.

[0111] It is understood that one or more of the above modules or units can be implemented by software, hardware, or a combination of both. When any of the above modules or units are implemented by software, the software exists as computer program instructions and is stored in memory. The processor can be used to execute the program instructions and implement the above method flow. The processor can be built into a system-on-a-chip (SoC) or an application-specific integrated circuit (ASIC), or it can be a separate semiconductor chip. In addition to the core that executes software instructions for computation or processing, the processor may further include necessary hardware accelerators, such as field-programmable gate arrays (FPGAs), programmable logic devices (PLDs), or logic circuits that implement dedicated logic operations.

[0112] When the above modules or units are implemented in hardware, the hardware can be any one or any combination of a CPU, microprocessor, digital signal processing (DSP) chip, microcontroller unit (MCU), artificial intelligence processor, ASIC, SoC, FPGA, PLD, application-specific digital circuit, hardware accelerator, or non-integrated discrete device, which can run the necessary software or perform the above method flow independently of software.

[0113] Optionally, this application also provides a chip system, including: at least one processor and an interface, wherein the at least one processor is coupled to a memory via the interface, and when the at least one processor executes a computer program or instructions in the memory, the method in any of the above method embodiments is executed. In one possible implementation, the chip system further includes a memory. Optionally, the chip system may be composed of chips or may include chips and other discrete devices; this application does not specifically limit this.

[0114] Optionally, this application also provides a computer-readable storage medium. All or part of the processes in the above method embodiments can be implemented by a computer program instructing related hardware. This program can be stored in the aforementioned computer-readable storage medium. When executed, the program can include the processes of the above method embodiments. The computer-readable storage medium can be an internal storage unit of the electronic device in any of the foregoing embodiments, such as a hard disk or memory of the electronic device. The aforementioned computer-readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk, smart memory card (SMC), secure digital card (SD), flash card, etc., equipped on the electronic device. Further, the aforementioned computer-readable storage medium can include both internal storage units and external storage devices of the electronic device. The aforementioned computer-readable storage medium is used to store the aforementioned computer program and other programs and data required by the aforementioned electronic device. The aforementioned computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0115] Optionally, this application also provides a computer program product. All or part of the processes in the above method embodiments can be executed by a computer program instructing related hardware. This program can be stored in the above computer program product, and when executed, it can include the processes described in the above method embodiments.

[0116] Optionally, this application also provides computer instructions. All or part of the processes in the above method embodiments can be executed by computer instructions to related hardware (such as a computer, processor, terminal, etc.). The program can be stored in the aforementioned computer-readable storage medium or the aforementioned computer program product.

[0117] Through the above description of the embodiments, those skilled in the art can clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual 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.

[0118] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0119] The units described as separate components may or may not be physically separate. A component shown as a unit can be one or more physical units; that is, it can be located in one place or distributed in multiple different locations. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0120] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0121] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An adaptive adjustment method, comprising: Determine whether the target-related first feature is met based on the first set of data. When the judgment result based on the first data does not meet the first feature, the judgment is based on the second data to determine whether the first condition is triggered. The second data is the data collected after the first data. When the judgment result based on the second data meets the triggering condition and the number of times the first condition is triggered exceeds the first threshold, the first threshold associated with the segment range where the target is located is adjusted to the second threshold based on the segment range. Both the first threshold and the second threshold are related to triggering the first condition, and the second threshold is more difficult to trigger the first condition than the first threshold.

2. The method according to claim 1, characterized in that, After adjusting the first threshold to the second threshold, the method further includes: Within a first time period, it is determined whether the first condition is met based on the third data, wherein the third data is the data collected within the first time period after the second data; When the judgment based on the third data meets the triggering condition, the second threshold is adjusted to the third threshold. The third threshold is related to the first condition, and the third threshold is more difficult to trigger the first condition than the second threshold. When the judgment based on the third data does not meet the triggering condition, the second threshold is adjusted to the fourth threshold. The fourth threshold is related to the first condition and is more likely to trigger the first condition than the second threshold. The fourth threshold is a value within the range of the first threshold and the second threshold.

3. The method according to claim 1, characterized in that, The method further includes: When the judgment result based on the first data matches the first feature, the corresponding business is executed based on the segment range where the target is located. The segment range is related to distance and belongs to a preset set of more than one segment range. The segment range in which the target is located is determined based on the first distance.

4. The method according to claim 3, characterized in that, The execution business functions include: When the target is within the nearest first segment range, perform face unlock; When the target is located in a relatively distant second segment range, or when face unlock fails, a lingering snapshot is performed.

5. The method according to claim 1, characterized in that, The first feature is the human movement feature.

6. The method according to claim 2, characterized in that, The first data, the second data, and the third data are sensor echo data of three frames or more, and the second data and the third data are greater than or equal to the first data.

7. The method according to claim 1, characterized in that, If the judgment result based on the second data does not meet the triggering condition, no action is taken, and the system waits for the next trigger to determine whether it meets the first feature.

8. The method according to claim 2, characterized in that, Adjusting the second threshold to the fourth threshold includes: The fourth threshold is adjusted based on its percentage relationship with the second threshold.

9. The method according to claim 1 or 7, characterized in that, The determination of whether a target meets the first characteristic is triggered based on the first data related to the target, including after the target is detected to be moving by a sensor.

10. The method according to any one of claims 6-9, characterized in that, The sensors include millimeter-wave radar, ultrasonic radar, or infrared ranging sensors used for human body detection.

11. An electronic device, characterized in that, The electronic device includes a unit or module for performing the method as described in any one of claims 1-10.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions, which, when executed, implement the method as described in any one of claims 1-10.

13. A computer program product containing instructions, characterized in that, When the computer program product is run on a computer, the method as described in any one of claims 1-10 is implemented.

14. An electronic device, characterized in that, include: A processor coupled to a memory for storing programs or instructions that, when executed by the processor, cause the apparatus to perform the method as described in any one of claims 1-10.

Images