Intelligent unlocking method based on biological feature recognition and environmental perception

Abstract

The invention relates to the technical field of intelligent door locks, and provides an intelligent unlocking method based on biological feature recognition and environment perception. The intelligent unlocking method comprises the steps that environment information around an intelligent door lock and a first biological feature of a user are obtained; evaluating the safety state of the environment of the intelligent door lock based on the environment information to obtain a target safety state, detecting abnormal activity signs of a user to obtain a detection result of the abnormal activity signs of the user, and integrating the target safety state and the detection result of the abnormal activity signs of the user into an environment sensing result; determining an unlocking risk rating according to the environment perception result, and determining an intelligent unlocking condition according to the unlocking risk rating and the environment perception result; and based on the intelligent unlocking condition, comparing the first biological feature with a pre-stored user feature template, and unlocking the intelligent door lock when the first biological feature is matched with the user feature template, or acquiring a second biological feature and unlocking the intelligent door lock when the first biological feature and the second biological feature are matched with the user feature template.

Description

Technical Field

[0001] The present application relates to the technical field of smart door locks, and more specifically, to a smart unlocking method based on biometric recognition and environmental perception. Background Art

[0002] With the development of smart home and Internet of Things technology, smart door locks are gradually replacing traditional door locks and becoming the main security equipment in modern homes and commercial places.

[0003] However, traditional smart door locks mainly rely on passwords, cards or single biometric features (such as fingerprints or facial recognition) for identity authentication. However, these methods have certain security risks, such as: passwords are easy to guess or leak; cards can be lost or stolen; single biometric recognition can be bypassed by forgery or without the user's knowledge. In addition, traditional smart door locks lack the ability to monitor the surrounding environment in real time, and cannot dynamically adjust security strategies according to environmental changes, resulting in insufficient overall security.

[0004] Therefore, how to improve the security of smart door locks is a problem that needs to be solved urgently. Summary of the invention

[0005] In view of the above problems existing in the prior art, the purpose of this application is to propose an intelligent unlocking method based on biometric recognition and environmental perception, so as to at least solve the technical problem of how to improve the security of smart door locks.

[0006] To achieve the above objectives and other related objectives, the present application provides a smart unlocking method based on biometric recognition and environmental perception, the method comprising: Acquire environmental information around the smart door lock and a first biometric feature of a user to unlock the smart door lock; Based on the environmental information, the security status of the environment around the smart door lock is evaluated to obtain a target security status, and abnormal user activity signs are detected to obtain abnormal user activity sign detection results, and the target security status and the abnormal user activity sign detection results are integrated into an environmental perception result; Determine an unlock risk rating according to the environmental perception result, and determine a smart unlock condition according to the unlock risk rating and the environmental perception result; Based on the smart unlocking condition, the first biometric feature is compared with a pre-stored user feature template, and the smart door lock is unlocked when the first biometric feature and the user feature template match, or the second biometric feature is obtained, and the smart door lock is unlocked when the first biometric feature and the second biometric feature and the user feature template match.

[0007] In some embodiments, before obtaining the first biometric feature of the user to unlock the smart door lock, the method further includes: Acquire multiple distances between the user and the smart door lock by using multiple distance detection modules pre-installed around the smart door lock; When the multiple distances are within a preset biometric feature collection distance range and remain within a preset time window, a pre-set biometric feature collection module is activated, and the biometric feature collection module is used to collect the first biometric feature and the second biometric feature.

[0008] In some embodiments, the biometric feature acquisition module includes a fingerprint feature acquisition submodule, a facial feature acquisition submodule, and an iris feature acquisition submodule, and the environmental information includes light intensity, temperature, humidity, and user distance. After acquiring the environmental information around the smart door lock, the following is further included: Obtaining a preset first influence weight of the light intensity on the biometric feature collection module, a second influence weight of the temperature on the biometric feature collection module, a third influence weight of the humidity on the biometric feature collection module, and a fourth influence weight of the user distance on the biometric feature collection module; Standardizing the light intensity, temperature, humidity, and user distance to obtain the standardized environmental information; According to the standardized environmental information, as well as the first influence weight, the second influence weight, the third influence weight, and the fourth influence weight, a first weighted average value corresponding to the fingerprint feature acquisition submodule, a second weighted average value corresponding to the facial feature acquisition submodule, and a third weighted average value corresponding to the iris feature acquisition submodule are obtained; According to the first weighted average value, adjusting the sensitivity parameter and exposure parameter of the fingerprint feature acquisition submodule; adjusting the exposure parameter and contrast parameter of the facial feature acquisition submodule according to the second weighted average value; According to the third weighted average value, the exposure parameter and the light source intensity parameter of the iris feature acquisition submodule are adjusted.

[0009] In some implementations, the evaluating the security status of the environment around the smart door lock based on the environmental information to obtain a target security status includes: Performing standardization processing on the environmental information to obtain standardized environmental information; The safety-related features in the standardized environmental information are combined into an environmental feature vector, and the environmental feature vector is transmitted to a pre-trained random forest model to obtain the target safety state.

[0010] In some implementations, performing abnormal user activity sign detection to obtain abnormal user activity sign detection results includes: Based on the environmental information, determine the approach speed, approach time period, and action posture of the user when approaching the smart door lock; The approach speed, approach time period, and action posture are compared with preset abnormal activity signs, and the abnormal activity sign detection result of the user is determined according to the abnormal activity sign comparison result.

[0011] In some implementations, the target security status includes safe, warning, and dangerous, the user abnormal activity sign detection result includes normal, suspicious, and abnormal, and determining the unlock risk rating according to the environment perception result includes: When the target security state is safe and the detection result of the abnormal activity sign of the user is normal, determining that the unlocking risk rating is low risk; When the target security status is a warning, or the detection result of the abnormal activity sign of the user is suspicious, determining that the unlocking risk rating is a medium risk; When the target security state is dangerous and the detection result of the abnormal activity sign of the user is abnormal, the unlocking risk rating is determined to be high risk.

[0012] In some implementations, determining the smart unlocking condition according to the unlocking risk rating and the environment perception result includes: When the unlock risk rating is low risk, the target security state is safe, and the detection result of the abnormal activity sign of the user is normal, setting the smart unlock condition to unlocking by using the first biometric feature; When the unlock risk rating is medium risk, the target security status is warning, or the detection result of the user's abnormal activity sign is suspicious, the smart unlock condition is set to unlocking through the first biometric feature and the second biometric feature; When the unlocking risk rating is high risk, the target security status is dangerous, and the detection result of the user's abnormal activity signs is abnormal, the smart unlocking condition is set to prohibit unlocking through the first biometric feature and the second biometric feature, and unlocking is performed through the unlocking control program or key pre-installed on the user's mobile terminal.

[0013] In one embodiment of the present application, a smart unlocking method and device based on biometric recognition and environmental perception is also provided, and the device includes: A data acquisition module, used to acquire environmental information around the smart door lock and a first biometric feature of a user to unlock the smart door lock; An environmental perception module is used to evaluate the security status of the environment around the smart door lock based on the environmental information to obtain a target security status, and to detect signs of abnormal user activities to obtain a detection result of abnormal user activities, and to integrate the target security status and the detection result of abnormal user activities into an environmental perception result; A risk rating module, used to determine an unlocking risk rating according to the environmental perception result, and to determine a smart unlocking condition according to the unlocking risk rating and the environmental perception result; The smart unlocking module is used to compare the first biometric feature with a pre-stored user feature template based on the smart unlocking condition, and unlock the smart door lock when the first biometric feature and the user feature template match, or to obtain a second biometric feature and unlock the smart door lock when the first biometric feature and the second biometric feature and the user feature template match.

[0014] In one embodiment of the present application, a computer-readable storage medium is also provided, wherein the computer-readable storage medium includes a stored computer program, wherein the computer program executes the above-mentioned smart unlocking method based on biometric recognition and environmental perception when running.

[0015] In one embodiment of the present application, an electronic device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the above-mentioned smart unlocking method based on biometric recognition and environmental perception through the computer program.

[0016] Beneficial effects of the present invention: First, obtain environmental information around the smart door lock and a first biometric feature of a user to unlock the smart door lock; then, based on the environmental information, evaluate the security status of the environment around the smart door lock to obtain a target security status, detect signs of abnormal user activity, and obtain a detection result of signs of abnormal user activity, and integrate the target security status and the detection result of signs of abnormal user activity into an environmental perception result; then, determine an unlocking risk rating based on the environmental perception result, and determine a smart unlocking condition based on the unlocking risk rating and the environmental perception result; finally, based on the smart unlocking condition, compare the first biometric feature with a pre-stored user feature template, and unlock the smart door lock when the first biometric feature and the user feature template match, or obtain a second biometric feature, and unlock the smart door lock when the first biometric feature and the second biometric feature and the user feature template match. In this application, a second biometric verification is introduced, which requires users to provide two different biometrics under certain circumstances, greatly increasing the difficulty of cracking; by evaluating the security status of the surrounding environment, potential threats or abnormal situations can be detected, and environmental perception can reject unlocking requests in unsafe situations, thereby improving security; user abnormal activity sign detection can identify the user's abnormal behavior patterns, and if signs of abnormal activity are detected, additional security measures can be triggered, such as alarms or further identity authentication requirements; the unlocking risk rating determined based on the environmental perception results changes dynamically, so that the smart door lock can respond to changes in external conditions in real time instead of operating based on fixed rules, which helps to cope with various complex situations and enhances adaptability and security; not only biometric matching is considered, but also the unlocking risk rating and environmental perception results are combined to determine the smart unlocking conditions. This method ensures that the unlocking operation is only performed when safety is confirmed, reducing the possibility of misjudgment and further improving the security of the smart door lock. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings: Figure 1 is a schematic diagram of an application environment of a smart unlocking method based on biometric recognition and environment perception shown in an exemplary embodiment of the present application; Figure 2 is a flowchart of a smart unlocking method based on biometric recognition and environmental perception shown in an exemplary embodiment of the present application; Figure 3 is a schematic diagram of an intelligent unlocking method and device based on biometric recognition and environmental perception, shown in an exemplary embodiment of the present application; Figure 4 It is a schematic diagram of the structure of an electronic device shown in an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0018] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0019] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0020] In one embodiment of the present application, a smart unlocking method based on biometric recognition and environmental perception is provided. Optionally, as an optional implementation, the smart unlocking method based on biometric recognition and environmental perception can be applied to, but is not limited to, Figure 1 in the environment shown. Figure 1 is a schematic diagram of an application environment of an intelligent unlocking method based on biometric recognition and environmental perception, shown in an exemplary embodiment of the present application. Figure 1, the implementation environment includes a smart door lock 110 and a server 120. The smart door lock 110 and the server 120 can communicate through a network, but are not limited to, and the server 103 can perform operations on the database, such as write data operations or read data operations, but are not limited to. Among them, the smart door lock 110 includes a data acquisition terminal 111 and a smart unlocking terminal 112. The above-mentioned smart unlocking terminal 112 may include, but is not limited to, a human-computer interaction screen, a processor and a memory. The above-mentioned human-computer interaction screen may be used, but is not limited to, to display the smart unlocking result. The above-mentioned processor may be used, but is not limited to, to respond to the above-mentioned human-computer interaction operation, perform corresponding operations, or generate corresponding instructions, and send the generated instructions to the cloud server 103. The above-mentioned memory is used to store relevant storage data, such as environmental information around the smart door lock, and the biometric features of the user to unlock the smart door lock.

[0021] As an optional method, data can be collected through the data collection terminal 111, such as collecting and preprocessing environmental information and biological characteristics.

[0022] As an optional method, the following steps in the smart unlocking method based on biometric recognition and environment perception may be performed on the smart unlocking terminal 112: Acquire environmental information around the smart door lock and a first biometric feature of a user to unlock the smart door lock; Based on the environmental information, the security status of the environment around the smart door lock is evaluated to obtain a target security status, and abnormal user activity signs are detected to obtain abnormal user activity sign detection results, and the target security status and the abnormal user activity sign detection results are integrated into an environmental perception result; Determine an unlock risk rating according to the environmental perception result, and determine a smart unlock condition according to the unlock risk rating and the environmental perception result; Based on the smart unlocking condition, the first biometric feature is compared with a pre-stored user feature template, and the smart door lock is unlocked when the first biometric feature and the user feature template match, or the second biometric feature is obtained, and the smart door lock is unlocked when the first biometric feature and the second biometric feature and the user feature template match.

[0023] The above method introduces a second biometric verification, requiring users to provide two different biometrics under certain circumstances, greatly increasing the difficulty of cracking; by evaluating the security status of the surrounding environment, potential threats or abnormal situations can be detected, and environmental perception can reject unlocking requests under unsafe conditions, thereby improving security; user abnormal activity sign detection can identify the user's abnormal behavior patterns. If signs of abnormal activity are detected, additional security measures can be triggered, such as alarms or further identity authentication requirements; the unlocking risk rating determined based on the environmental perception results changes dynamically, so that the smart door lock can respond to changes in external conditions in real time instead of operating based on fixed rules, which helps to cope with various complex situations and enhance adaptability and security; not only biometric matching is considered, but also the unlocking risk rating and environmental perception results are combined to determine the smart unlocking conditions. This method ensures that the unlocking operation is only performed when safety is confirmed, reducing the possibility of misjudgment and further improving the security of the smart door lock.

[0024] Optionally, in this embodiment, the network may include but is not limited to: a wireless network, wherein the wireless network includes: Bluetooth, WIFI and other networks that implement wireless communication. The server may be a single server or a server cluster composed of multiple servers. The above is only an example and is not limited in this embodiment.

[0025] As an optional example, this embodiment does not limit the execution subject of the above-mentioned smart unlocking method based on biometric recognition and environmental perception. Some or all steps of the above-mentioned smart unlocking method based on biometric recognition and environmental perception can be executed on the smart unlocking terminal 112.

[0026] In one embodiment of the present application, a smart unlocking method based on biometric recognition and environmental perception is provided. Figure 2 is a flowchart of an intelligent unlocking method based on biometric recognition and environmental perception, shown in an exemplary embodiment of the present application. Figure 2 The smart unlocking method based on biometric recognition and environmental perception includes the following steps S210 to S240: In step S210, environmental information around the smart door lock and a first biometric feature of a user to unlock the smart door lock are obtained.

[0027] Among them, the environmental information around the smart door lock refers to the state of the physical environment around the smart door lock, including but not limited to lighting conditions, temperature, humidity, sound level, whether there are other people, etc. In addition, it can also include video images or audio recordings to analyze whether there are abnormal activities or potential threats. This data is usually collected through sensors installed near the door lock (such as cameras, microphones, infrared sensors, temperature and humidity sensors, etc.). For example, a camera can capture the image of a visitor, a microphone can record the surrounding sound, and an infrared sensor can detect human body heat to determine whether there is a living person approaching.

[0028] Among them, the first biometric feature refers to the physiological or behavioral characteristics used to initially identify the user, such as fingerprints, facial features, iris patterns, palm prints, voice prints, or walking posture. These features are unique and can be used to verify the user's identity. Biometric features are usually collected through specialized sensors or devices. For example, fingerprints can be obtained through fingerprint scanners; facial features can be extracted through cameras and facial recognition algorithms; iris patterns require specific iris scanning equipment.

[0029] For example, there is a smart door lock installed at the door of a house, equipped with the following components: a high-definition camera to capture the facial image of the visitor and monitor the situation around the door lock; a micro microphone to record the surrounding sounds to help detect whether there is a conversation or abnormal noise; an infrared motion sensor to sense whether someone is approaching the door lock; a temperature and humidity sensor to record the temperature and humidity changes in the environment; a fingerprint scanner to read the user's fingerprint as the first biometric feature. When the user walks to the door of the house, the infrared motion sensor detects someone approaching and triggers the smart door lock to enter the ready state; the high-definition camera starts recording video to capture the user and the surrounding environment. The camera may also use face recognition technology to try to identify the user; the micro microphone starts the recording function to monitor the surrounding sounds, such as whether there are other people talking or other abnormal sounds; the temperature and humidity sensor provides the current environmental conditions, which helps to determine whether the door lock is operated under normal conditions (for example, extreme weather may cause some sensors to false alarm).

[0030] In step S220, based on the environmental information, the security status of the environment around the smart door lock is evaluated to obtain a target security status, and abnormal user activity signs are detected to obtain abnormal user activity sign detection results, and the target security status and the abnormal user activity sign detection results are integrated into an environmental perception result.

[0031] Among them, evaluating the security status of the environment around the smart door lock involves analyzing the environmental information collected from various sensors to determine whether the current environment is safe. The evaluation may be based on preset security standards or models, which take into account a variety of factors, such as whether there are other people, whether there is abnormal noise, whether the lighting conditions are normal, etc., and machine learning algorithms or preset rule bases can be used to process and parse sensor data. For example, image recognition algorithms can be used to detect whether there are strangers present; audio analysis can identify abnormal sounds (such as quarrels or the sound of destroying tools); data from temperature and humidity sensors can help eliminate false alarms caused by extreme weather.

[0032] Among them, the detection of signs of abnormal user activity is aimed at identifying atypical user behavior patterns, which may indicate that the user is under duress or is conducting an attempted attack. Abnormal activities may include but are not limited to the user's facial expressions, body posture, movement speed, and the way of interacting with the door lock. Through deep learning and computer vision technology, user behavior in the video stream can be analyzed. For example, facial recognition technology can detect the user's nervousness (such as dilated pupils and tense facial muscles); motion recognition algorithms can capture unnatural movements (such as hesitant gestures and excessive head turns); and the speed and method of the user's interaction with the door lock can also be monitored, such as whether it is too hasty or slow.

[0033] Among them, integrating the target security status and the detection results of the signs of abnormal user activities into the environmental perception result is to combine all the collected and analyzed information to form a comprehensive environmental perception result. This result not only reflects the security status of the current environment, but also includes the user's behavior status, so that it can more accurately determine whether unlocking should be allowed. Multimodal fusion technology and decision tree algorithm are used to correlate and analyze data from different sources (such as images, audio, biometrics, etc.). For example, if the environment is assessed as safe but the user exhibits abnormal behavior, vigilance may be increased and additional verification steps may be required; on the contrary, if the environment is considered dangerous, the unlock request may be rejected even if the user's behavior seems normal.

[0034] For example, when the user walks to the door lock, the infrared motion sensor detects that someone is approaching, triggering other sensors to start working; the high-definition camera starts recording and uses facial recognition technology to confirm the user's identity. At the same time, the camera also captures the figure of another person in the background; the microphone records the surrounding sounds and finds a slight conversation, which may be the background person talking quietly; the temperature and humidity sensor shows that the current environmental conditions are normal and there is no extreme weather. The facial recognition and emotion analysis module detects that the user is anxious and his eyes are quickly scanning around, which may be nervous; the action recognition module notices that the user's fingers are trembling when entering the password, and the movements are faster than usual. It is also observed that the user looks back at the background person several times, as if confirming the other party's location; combining all the above information, it is concluded that although the environment itself does not have obvious threats (such as loud noises, violent behavior), the user shows obvious uneasiness and tension, and there may be a risk of being coerced; based on this comprehensive assessment, it is decided not to unlock the door lock directly, but to prompt the user to perform a secondary verification (such as voice verification or sending a one-time verification code to the user's mobile phone). If the user fails to complete the secondary verification, an alarm will be automatically triggered and the video clip will be sent to the property management staff or the police.

[0035] In step S230, an unlocking risk rating is determined according to the environmental perception result, and a smart unlocking condition is determined according to the unlocking risk rating and the environmental perception result.

[0036] Among them, according to the environmental perception results, the unlocking risk rating is determined. This process is to evaluate the security risk level in the current situation based on the environmental perception results of comprehensive analysis. The risk rating can be divided into multiple levels (such as low, medium, and high), and each level corresponds to a different degree of security threat. Risk assessment is performed by combining multiple factors using a machine learning model or a preset rule base. These factors may include, but are not limited to: the number and identity of people in the environment (whether there are strangers, whether they are known visitors); the type and intensity of surrounding sounds (whether there are abnormal noises, conversations, etc.); the user's behavior pattern (such as facial expressions, movement speed, and the way of interacting with the door lock); the current time period (for example, nighttime activities may be considered more suspicious); weather conditions and other environmental factors (such as extreme weather may cause false alarms). Finally, an unlocking risk rating is generated as the basis for subsequent decisions.

[0037] After the risk rating is determined, specific unlocking conditions need to be set based on this rating and previous environmental perception results. These conditions may be standard (such as only requiring biometric matching) or enhanced (such as requiring additional authentication steps).

[0038] In step S240, based on the smart unlocking condition, the first biometric feature is compared with a pre-stored user feature template, and the smart door lock is unlocked when the first biometric feature and the user feature template match, or the second biometric feature is obtained, and the smart door lock is unlocked when the first biometric feature and the second biometric feature and the user feature template match.

[0039] Among them, user feature templates refer to pre-stored user biometric data, which are obtained and processed by specific collection devices (such as fingerprint scanners, facial recognition cameras, iris scanners, etc.). Each user's feature template is unique and is used for subsequent identity verification. Feature templates usually contain biometric key points or feature vectors extracted and compressed by algorithms. For example, fingerprint templates may include features such as fingerprint ridges, bifurcation points, and terminal points; facial feature templates may include geometric features such as eye spacing, nose bridge shape, and mouth contour. These templates are securely stored in local databases or cloud servers and can only be accessed by authorized personnel.

[0040] The second biometric is another type of biometric that the user provides when additional verification is required. This is usually designed to increase security, ensuring that even if the first biometric is copied or tampered with, it can still be verified through the second feature. The second biometric can be a different type from the first biometric, for example, if the first biometric is a fingerprint, the second biometric can be facial recognition, iris scan, voiceprint recognition, or palmprint recognition. Users can be asked to provide both features and compare them with pre-stored feature templates to confirm the user's identity.

[0041] By adopting the above-mentioned embodiment provided by the present application, a second biometric verification is introduced, and the user is required to provide two different biometrics under certain circumstances, which greatly increases the difficulty of cracking; by evaluating the security status of the surrounding environment, potential threats or abnormal situations can be detected, and environmental perception can reject unlocking requests under unsafe conditions, thereby improving security; detection of signs of abnormal user activity can identify the user's abnormal behavior patterns. If signs of abnormal activity are detected, additional security measures can be triggered, such as alarms or requirements for further identity authentication; the unlocking risk rating determined according to the environmental perception results changes dynamically, so that the smart door lock can respond to changes in external conditions in real time, rather than operating based on fixed rules, which helps to cope with various complex situations and enhances adaptability and security; not only biometric matching is considered, but also the unlocking risk rating and environmental perception results are combined to determine the smart unlocking conditions. This method ensures that the unlocking operation is performed only when safety is confirmed, reduces the possibility of misjudgment, and further improves the security of the smart door lock.

[0042] In one embodiment of the present application, before obtaining the first biometric feature of the user to unlock the smart door lock, the method further includes: Acquire multiple distances between the user and the smart door lock by using multiple distance detection modules pre-installed around the smart door lock; When the multiple distances are within a preset biometric feature collection distance range and remain within a preset time window, a pre-set biometric feature collection module is activated, and the biometric feature collection module is used to collect the first biometric feature and the second biometric feature.

[0043] Among them, multiple distance detection modules are used to accurately measure the distance between the user and the smart door lock. Each distance detection module can be an independent sensor, such as an ultrasonic sensor, an infrared sensor, a laser rangefinder, etc., distributed in multiple locations around the door lock to ensure that the user's position is accurately captured from different angles. Through multiple distance detection modules, three-dimensional spatial information between the user and the door lock can be obtained. For example, one module may be installed above the door lock, another on the side, and another at the bottom. These modules work together to provide more comprehensive distance data to help determine whether the user is in a suitable collection range.

[0044] The preset time window refers to the maximum time that the user is allowed to stay within the biometric collection range. This time window is to prevent false triggering or unnecessary activation, ensuring that biometric collection is only initiated when the user intentionally approaches and stays. The time window is usually a fixed time period (such as 5 seconds, 10 seconds, etc.), which is set according to the application scenario and security requirements. If the user stays within the preset distance within this time period, it will be considered that the user intentionally unlocks and activates the biometric collection module; otherwise, the collection process will not be initiated to avoid resource waste and misoperation.

[0045] Among them, activating the pre-set biometric collection module means automatically activating the device or module used to collect biometrics after meeting certain conditions (such as the user enters a preset distance range and stays within a preset time). These modules can include fingerprint scanners, facial recognition cameras, iris scanners, etc. Biometric collection modules are usually low-power and are in standby mode when not activated. When it is detected that the user meets the activation conditions, a signal will be sent to wake up these modules, so that they enter the working state and start collecting the user's biometrics. This method not only improves the response speed, but also extends the battery life and device life.

[0046] For example, there is a smart door lock installed at the entrance of a high-end residential area, which is equipped with the following components: multiple distance detection modules, including three ultrasonic sensors, which are installed above, on the left and on the right of the door lock, respectively, to measure the distance between the user and the door lock; a high-definition camera, which is used to collect the user's facial image as the second biometric feature; a fingerprint scanner, which is used to collect the user's fingerprint as the first biometric feature; a microphone, which records the surrounding sound; a temperature and humidity sensor, which records the temperature and humidity of the environment; a multimodal fusion module, which is used to comprehensively analyze multiple sensor data to evaluate the environmental security status and user behavior. When the user walks to the door lock, the three ultrasonic sensors begin to measure the distance between the user and the door lock. The preset biometric collection distance range is set to 0.5 meters to 1.5 meters. If the user stays in this range for more than 5 seconds (preset time window), it is considered that the user intends to unlock the door. When it is detected that the user has stayed in the preset distance range for a long enough time, the fingerprint scanner and facial recognition camera are immediately activated. The fingerprint scanner begins to prepare to collect the user's fingerprint, and the facial recognition camera begins to capture the user's facial image. The user places his finger on the fingerprint scanner, collects the fingerprint data and compares it with the pre-stored template. The user faces the facial recognition camera, collects the facial image and compares it with the pre-stored template. If both the fingerprint and facial features match successfully, the user's identity is confirmed and the door lock is unlocked; if any feature does not match, the door lock remains locked and the user is prompted to try again or select another verification method.

[0047] In this embodiment, through multiple distance detection modules, it is possible to more accurately determine whether the user really intends to unlock, reducing the possibility of false triggering. The biometric acquisition module will only be activated when the user stays within the preset distance range for a long enough time, thereby preventing malicious personnel from using short proximity to trigger activation. The design of the preset time window ensures that it will not be activated when the user passes by unintentionally, avoiding unnecessary disturbances. The acquisition process will only be started when the user intentionally approaches and stays, which improves the convenience and naturalness of use. The biometric acquisition module is in a low-power standby mode when it is not activated, and will only be awakened when specific conditions are met. This design not only extends the battery life of the device, but also reduces unnecessary energy consumption and improves overall efficiency. Multiple distance detection modules provide redundant and more comprehensive distance information, which can more reliably determine the user's location. Even if a sensor fails or is interfered with, other sensors can still work normally to ensure stability and reliability. Through precise distance detection and time window control, tailing (i.e., unauthorized people follow authorized users to enter) can be effectively prevented. In addition, it can also be combined with environmental perception functions to further improve security and prevent other types of physical attacks.

[0048] In one embodiment of the present application, the biometric feature acquisition module includes a fingerprint feature acquisition submodule, a facial feature acquisition submodule, and an iris feature acquisition submodule, and the environmental information includes light intensity, temperature, humidity, and user distance. After acquiring the environmental information around the smart door lock, it also includes: Obtaining a preset first influence weight of the light intensity on the biometric feature collection module, a second influence weight of the temperature on the biometric feature collection module, a third influence weight of the humidity on the biometric feature collection module, and a fourth influence weight of the user distance on the biometric feature collection module; Standardizing the light intensity, temperature, humidity, and user distance to obtain the standardized environmental information; According to the standardized environmental information, as well as the first influence weight, the second influence weight, the third influence weight, and the fourth influence weight, a first weighted average value corresponding to the fingerprint feature acquisition submodule, a second weighted average value corresponding to the facial feature acquisition submodule, and a third weighted average value corresponding to the iris feature acquisition submodule are obtained; According to the first weighted average value, adjusting the sensitivity parameter and exposure parameter of the fingerprint feature acquisition submodule; adjusting the exposure parameter and contrast parameter of the facial feature acquisition submodule according to the second weighted average value; According to the third weighted average value, the exposure parameter and the light source intensity parameter of the iris feature acquisition submodule are adjusted.

[0049] Among them, the fingerprint feature acquisition submodule is a device used to collect user fingerprints, usually including optical or capacitive sensors. These sensors can capture fingerprint features such as ridges, bifurcation points, and terminal points. The fingerprint feature acquisition submodule obtains the user's fingerprint image through a high-resolution sensor and converts it into a digital signal for processing and storage.

[0050] Among them, the facial feature acquisition submodule is used to collect the user's facial image camera, which is usually equipped with an infrared camera and can capture the key feature points of the face, such as the distance between the eyes, the shape of the bridge of the nose, the outline of the mouth, etc. The facial feature acquisition submodule obtains the user's facial image through a high-definition camera and extracts the facial feature vector using a computer vision algorithm.

[0051] Among them, the iris feature acquisition submodule is a dedicated camera used to collect user iris images, which is usually equipped with a near-infrared light source and a high-resolution sensor. Iris features are extremely unique and suitable for high-security identity authentication. The iris feature acquisition submodule obtains iris images through a near-infrared camera and uses a specific algorithm to extract iris texture features.

[0052] During the standardization process, environmental information of different ranges (such as light intensity, temperature, humidity, and user distance) is converted into numerical values ​​within the same standard range to facilitate subsequent weighted calculations and comparisons. Linear or nonlinear transformation methods are usually used to map the original data to standardized values ​​between 0 and 1.

[0053] The weighted average is a comprehensive score calculated based on the weight of each environmental factor on the biometric collection module, which is used to adjust the working parameters of each module. The weighted average of each biometric collection submodule is obtained by multiplying the standardized environmental information by the corresponding influence weight, then summing and averaging.

[0054] Among them, according to the weighted average value of the fingerprint feature acquisition submodule, its sensitivity and exposure parameters are dynamically adjusted to optimize the acquisition effect. If the light is dim, the exposure time may be increased; if the finger is wet, the sensitivity may be reduced to reduce false touches.

[0055] Among them, according to the weighted average of the facial feature acquisition submodule, its exposure and contrast parameters are dynamically adjusted to ensure the clarity and accuracy of the facial image. If the light is too strong or too weak, the exposure parameters will be adjusted; if the humidity is high, the contrast may be increased to improve the image quality.

[0056] Among them, according to the weighted average value of the iris feature acquisition submodule, its exposure and light intensity parameters are dynamically adjusted to ensure the clarity and details of the iris image. If the light is insufficient, the light intensity will be increased; if the user is far away, the exposure time may be adjusted to obtain a clearer iris image.

[0057] In this embodiment, by considering the impact of environmental factors on the biometric acquisition module and dynamically adjusting relevant parameters, high-quality biometric data can be obtained in various environments, thereby improving the accuracy and reliability of recognition, and can automatically adjust the acquisition parameters according to different environmental conditions to adapt to various complex usage scenarios, such as light changes, temperature fluctuations, humidity differences, etc., to ensure normal operation in any situation. Dynamic adjustment of acquisition parameters not only improves the success rate of recognition, but also reduces the user's waiting time and operation complexity. Users do not need to manually adjust the device, and the acquisition process will be automatically optimized, providing a smoother and more natural user experience.

[0058] In one embodiment of the present application, the security state of the environment around the smart door lock is evaluated based on the environmental information to obtain a target security state, including: Performing standardization processing on the environmental information to obtain standardized environmental information; The safety-related features in the standardized environmental information are combined into an environmental feature vector, and the environmental feature vector is transmitted to a pre-trained random forest model to obtain the target safety state.

[0059] Among them, the security-related features in the standardized environmental information refer to the data extracted from the original environmental information that have a direct impact on the assessment of the environmental safety status. They are standardized to ensure that data of different dimensions can be compared and analyzed on the same scale. Common security-related features include light intensity, temperature, humidity, user distance, ambient sound level, whether there are strangers, etc. These feature values ​​are mapped to a standardized range between 0 and 1 through linear or nonlinear transformation methods. For example, the temperature from 0°C to 40°C is standardized to a value between 0 and 1.

[0060] Among them, random forest is an integrated learning method composed of multiple decision trees. Each decision tree is trained based on randomly selected samples and features, and finally the prediction result is obtained through a voting mechanism. Random forest performs well in processing high-dimensional data and preventing overfitting, and is suitable for classification and regression tasks. In this embodiment, the random forest model is used to evaluate the environmental safety status around the smart door lock based on the environmental feature vector. The model is trained with a large amount of historical data to learn the relationship between different combinations of environmental features and safety status. After training, the model can quickly and accurately classify new environmental feature vectors and output the target safety status (such as "safe", "warning", "dangerous").

[0061] Among them, when the environmental feature vector is transmitted to the pre-trained random forest model to obtain the target safety state, it means that the standardized environmental feature vector is input into the pre-trained random forest model, and the model will classify according to the input feature vector and output a result representing the current environmental safety state. The environmental feature vector is passed to the random forest model through an interface or direct memory transmission. Multiple decision trees within the model will independently evaluate the input feature vector and determine the final safety state through a majority voting mechanism. The output target safety state can be a binary classification (such as "safe" or "unsafe") or a multi-class classification (such as "safe", "warning", "dangerous"), depending on the needs of the application scenario.

[0062] In this embodiment, by using the random forest model, it is possible to comprehensively consider multiple environmental factors and accurately assess the security status of the current environment. Compared with a single rule or simple threshold judgment, the random forest model can better cope with complex environmental changes and reduce the possibility of misjudgment and omission. The random forest model has a strong generalization ability and can adapt to different environmental conditions and scenarios. Even when conditions such as light, temperature, and humidity change, it can still accurately assess the security status and ensure that reliable security can be provided in all situations. The reasoning process of the random forest model is very fast, and it can complete the classification of environmental feature vectors in a short time, give the security status assessment results in a timely manner, and respond quickly to take corresponding security measures to prevent the occurrence of potential threats.

[0063] In one embodiment of the present application, the performing of abnormal user activity sign detection to obtain abnormal user activity sign detection results includes: Based on the environmental information, determine the approach speed, approach time period, and action posture of the user when approaching the smart door lock; The approach speed, approach time period, and action posture are compared with preset abnormal activity signs, and the abnormal activity sign detection result of the user is determined according to the abnormal activity sign comparison result.

[0064] Among them, approach speed refers to the speed at which a user approaches a smart door lock from a distance. Approach speed can reflect the user's intentions and behavior patterns. For example, running fast may indicate an emergency or abnormal behavior, while walking slowly may be a normal approach. Ultrasonic sensors, infrared sensors, or cameras installed around the door lock can monitor the user's position changes in real time and calculate their approach speed. Sensor data can be used to determine the user's speed through time series analysis.

[0065] Among them, the proximity time period refers to the specific time period when the user is close to the smart door lock. The activity patterns in different time periods may be different. For example, nighttime activities may require more attention than daytime activities because nighttime is usually a period of higher security risks. The time when the user is close to the door lock is recorded through the built-in clock module or synchronized with the network time. Combined with historical data and statistical analysis, abnormal time period activity patterns can be identified.

[0066] Among them, action posture refers to the body posture and movement of the user when approaching the smart door lock. This information can help determine whether the user's behavior is normal. For example, frequent turning back, nervous expressions, unnatural gestures, etc. may be abnormal signs. Through high-definition cameras and computer vision technology (such as posture estimation and action recognition algorithms), the user's action posture can be captured and analyzed. Deep learning models can be used to identify specific action patterns, such as quick turns and hesitant steps.

[0067] Exemplarily, the current environmental information, including the distance between the user and the door lock, the body heat distribution, the video image and the sound level, is obtained through an ultrasonic distance sensor, an infrared sensor, a high-definition camera and a microphone. The ultrasonic distance sensor monitors the user's position change in real time and calculates the user's approach speed through time series analysis. For example, if the user moves from 2 meters away to the door lock within 5 seconds, the calculation result shows that the approach speed is 0.4 meters per second. The clock module records the specific time period when the user approaches the door lock. For example, the user approaches the door lock at 2:30 in the morning, which is generally considered a high-risk time period. The high-definition camera captures the user's facial expressions and gestures, and uses computer vision algorithms for analysis. For example, it is detected that the user is anxious and his eyes quickly scan around, which may be nervous; at the same time, the user's fingers tremble when entering the password, and the movements are faster than usual. In addition, the user looks back in a certain direction many times, as if confirming the surrounding situation. The above-determined approach speed, approach time period and action posture are compared with the preset signs of abnormal activities. For example: if the approach speed exceeds the preset threshold (such as 0.5 m / s), it is considered that the user may be running or approaching in a hurry, which may be abnormal behavior; if the user approaches the door lock during a high-risk time period (such as late at night or early in the morning), it will be more vigilant; if the user is detected to show obvious uneasiness and tension (such as frequent turning back, nervous expression, unnatural movements), this may be a sign of coercion or other abnormal conditions. Based on the results of the abnormal activity sign comparison, the user's abnormal activity sign detection results are obtained. For example: if the user approaches at a fast speed, approaches during a high-risk time period, and shows obvious tension and uneasiness, it may be judged as "abnormal"; if the user approaches at a moderate speed, approaches during a normal time period, and shows normal movements and postures, it may be judged as "normal".

[0068] In this embodiment, by comprehensively analyzing the user's approach speed, time period, and action posture, abnormal behavior can be identified more accurately, reducing the possibility of misjudgment and missed reports. Especially in high-risk time periods or when users show obvious abnormalities, timely measures can be taken to prevent the occurrence of potential threats. The detection strategy can be dynamically adjusted according to different environmental conditions and user behavior patterns to adapt to various complex usage scenarios.

[0069] In an embodiment of the present application, the target security status includes safe, warning, and dangerous, the user abnormal activity sign detection result includes normal, suspicious, and abnormal, and the determining of the unlocking risk rating according to the environmental perception result includes: When the target security state is safe and the detection result of the abnormal activity sign of the user is normal, determining that the unlocking risk rating is low risk; When the target security status is a warning, or the detection result of the abnormal activity sign of the user is suspicious, determining that the unlocking risk rating is a medium risk; When the target security state is dangerous and the detection result of the abnormal activity sign of the user is abnormal, the unlocking risk rating is determined to be high risk.

[0070] In this embodiment, by comprehensively considering the target security status and the detection results of abnormal user activity signs, the risk level in the current situation can be more accurately assessed. This multi-factor assessment mechanism significantly improves security and reduces the possibility of misjudgment and omission. Especially in high-risk situations, timely measures can be taken to prevent the occurrence of potential threats. The unlocking risk rating can be dynamically adjusted according to different environmental conditions and user behavior patterns to adapt to various complex usage scenarios. For example, the evaluation criteria can be different during the day and at night to ensure reliable security in any situation.

[0071] In an embodiment of the present application, determining the smart unlocking condition according to the unlocking risk rating and the environmental perception result includes: When the unlock risk rating is low risk, the target security state is safe, and the detection result of the abnormal activity sign of the user is normal, setting the smart unlock condition to unlocking by using the first biometric feature; When the unlock risk rating is medium risk, the target security status is warning, or the detection result of the user's abnormal activity sign is suspicious, the smart unlock condition is set to unlocking through the first biometric feature and the second biometric feature; When the unlocking risk rating is high risk, the target security status is dangerous, and the detection result of the user's abnormal activity signs is abnormal, the smart unlocking condition is set to prohibit unlocking through the first biometric feature and the second biometric feature, and unlocking is performed through the unlocking control program or key pre-installed on the user's mobile terminal.

[0072] It can be seen from the above embodiments that a second biometric verification is introduced, and users are required to provide two different biometrics under certain circumstances, which greatly increases the difficulty of cracking; by evaluating the security status of the surrounding environment, potential threats or abnormal situations can be detected, and environmental perception can reject unlocking requests under unsafe conditions, thereby improving security; user abnormal activity sign detection can identify the user's abnormal behavior patterns. If signs of abnormal activity are detected, additional security measures can be triggered, such as alarms or further identity authentication requirements; the unlocking risk rating determined based on the environmental perception results changes dynamically, so that the smart door lock can respond to changes in external conditions in real time instead of operating based on fixed rules, which helps to cope with various complex situations and enhances adaptability and security; not only biometric matching is considered, but also the unlocking risk rating and environmental perception results are combined to determine the smart unlocking conditions. This method ensures that the unlocking operation is only performed when safety is confirmed, reducing the possibility of misjudgment and further improving the security of the smart door lock.

[0073] In one embodiment of the present application, a smart unlocking method and device based on biometric recognition and environmental perception is also provided. Figure 3 is a schematic diagram of an intelligent unlocking method and device based on biometric recognition and environmental perception, shown in an exemplary embodiment of the present application. Figure 3 , the device comprises: The data acquisition module 301 is used to acquire the environmental information around the smart door lock and the first biometric feature of the user to unlock the smart door lock; The environment perception module 302 is used to evaluate the security status of the environment around the smart door lock based on the environmental information, obtain a target security status, and detect abnormal user activity signs to obtain a user abnormal activity sign detection result, and integrate the target security status and the user abnormal activity sign detection result into an environment perception result; The risk rating module 303 is used to determine an unlocking risk rating according to the environmental perception result, and determine a smart unlocking condition according to the unlocking risk rating and the environmental perception result; The smart unlocking module 304 is used to compare the first biometric feature with a pre-stored user feature template based on the smart unlocking condition, and unlock the smart door lock when the first biometric feature and the user feature template match, or to obtain a second biometric feature and unlock the smart door lock when the first biometric feature and the second biometric feature and the user feature template match.

[0074] The smart unlocking method and device based on biometric identification and environmental perception in the embodiment of the present application introduces a second biometric verification, requiring the user to provide two different biometrics under certain circumstances, which greatly increases the difficulty of cracking; by evaluating the security status of the surrounding environment, potential threats or abnormal situations can be detected, and environmental perception can reject unlocking requests under unsafe conditions, thereby improving security; user abnormal activity sign detection can identify the user's abnormal behavior pattern, and if abnormal activity signs are detected, additional security measures can be triggered, such as alarms or further identity authentication requirements; the unlocking risk rating determined according to the environmental perception result changes dynamically, so that the smart door lock can respond to changes in external conditions in real time instead of operating based on fixed rules, which helps to cope with various complex situations and enhance adaptability and security; not only biometric matching is considered, but also the unlocking risk rating and environmental perception results are combined to determine the smart unlocking conditions. This method ensures that the unlocking operation is performed only when safety is confirmed, reducing the possibility of misjudgment and further improving the security of the smart door lock.

[0075] The specific embodiments of the smart unlocking method and device based on biometric recognition and environmental perception in this application can refer to the example shown in the above-mentioned smart unlocking method based on biometric recognition and environmental perception, which will not be repeated here in this example.

[0076] In one embodiment of the present application, an electronic device for implementing the above-mentioned smart unlocking method based on biometric recognition and environmental perception is also provided. The electronic device includes a memory and a processor, the memory stores a computer program, and the processor is configured to execute the above-mentioned smart unlocking method based on biometric recognition and environmental perception through the computer program.

[0077] See also Figure 4 , Figure 4 4 is a schematic diagram of the structure of an electronic device shown in an exemplary embodiment of the present application. The computer system 400 includes a central processing unit (CPU) 401, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 402 or the program loaded from the storage part 408 to the random access memory (RAM) 403, such as executing the method described in the above embodiment. In RAM 403, various programs and data required for system operation are also stored. CPU 401, ROM 402 and RAM 403 are connected to each other through bus 404. Input / output (I / O) interface 405 is also connected to bus 404.

[0078] The following components are connected to the I / O interface 405: an input section 406 including such as a keyboard, a mouse, etc.; an output section 407 including such as a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the I / O interface 405 as needed. A removable medium 411, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 410 as needed so that a computer program read therefrom is installed into the storage section 408 as needed.

[0079] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 409, and / or installed from a removable medium 411. When the computer program is executed by the central processing unit (CPU) 401, various functions defined in the system of the present application are executed.

[0080] It should be noted that the computer-readable medium shown in the embodiment of the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier, which carries a computer-readable computer program. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. A computer program contained on a computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0081] The flowchart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the system, method and computer program product according to various embodiments of the present application. Wherein, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0082] The units involved in the embodiments described in this application may be implemented by software or hardware, and the units described may also be set in a processor. The names of these units do not, in some cases, constitute limitations on the units themselves.

[0083] Another aspect of the present application further provides a computer-readable storage medium, the computer-readable storage medium including a stored computer program, wherein the computer program executes the above-mentioned smart unlocking method based on biometric recognition and environmental perception when it is run. The computer-readable storage medium may be included in the electronic device described in the above embodiment, or may exist independently without being assembled into the electronic device.

[0084] Another aspect of the present application further provides a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the smart unlocking method based on biometric recognition and environmental perception provided in the above-mentioned embodiments.

[0085] The above embodiments are merely illustrative of the principles and effects of the present invention, and are not intended to limit the present invention. Anyone familiar with the technology may modify or change the above embodiments without violating the spirit and scope of the present invention. Therefore, all equivalent modifications or changes made by a person of ordinary skill in the art without departing from the spirit and technical ideas disclosed by the present invention shall still be covered by the claims of the present invention.

Claims

1. An intelligent unlocking method based on biometric recognition and environmental perception, characterized in that: The method comprises: Acquire environmental information around the smart door lock and a first biometric feature of a user to unlock the smart door lock; Based on the environmental information, the security status of the environment around the smart door lock is evaluated to obtain a target security status, and abnormal user activity signs are detected to obtain abnormal user activity sign detection results, and the target security status and the abnormal user activity sign detection results are integrated into an environmental perception result; Determine an unlock risk rating according to the environmental perception result, and determine a smart unlock condition according to the unlock risk rating and the environmental perception result; Based on the smart unlocking condition, the first biometric feature is compared with a pre-stored user feature template, and the smart door lock is unlocked when the first biometric feature and the user feature template match, or the second biometric feature is obtained, and the smart door lock is unlocked when the first biometric feature and the second biometric feature and the user feature template match.

2. The smart unlocking method based on biometric recognition and environmental perception as claimed in claim 1, characterized in that: Before obtaining the first biometric feature of the user to unlock the smart door lock, the method further includes: Acquire multiple distances between the user and the smart door lock by using multiple distance detection modules pre-installed around the smart door lock; When the multiple distances are within a preset biometric feature collection distance range and remain within a preset time window, a pre-set biometric feature collection module is activated, and the biometric feature collection module is used to collect the first biometric feature and the second biometric feature.

3. The smart unlocking method based on biometric recognition and environmental perception as claimed in claim 2, characterized in that: The biometric feature acquisition module includes a fingerprint feature acquisition submodule, a facial feature acquisition submodule, and an iris feature acquisition submodule. The environmental information includes light intensity, temperature, humidity, and user distance. After acquiring the environmental information around the smart door lock, it also includes: Obtaining a preset first influence weight of the light intensity on the biometric feature collection module, a second influence weight of the temperature on the biometric feature collection module, a third influence weight of the humidity on the biometric feature collection module, and a fourth influence weight of the user distance on the biometric feature collection module; Standardizing the light intensity, temperature, humidity, and user distance to obtain the standardized environmental information; According to the standardized environmental information, as well as the first influence weight, the second influence weight, the third influence weight, and the fourth influence weight, a first weighted average value corresponding to the fingerprint feature acquisition submodule, a second weighted average value corresponding to the facial feature acquisition submodule, and a third weighted average value corresponding to the iris feature acquisition submodule are obtained; According to the first weighted average value, adjusting the sensitivity parameter and exposure parameter of the fingerprint feature acquisition submodule; adjusting the exposure parameter and contrast parameter of the facial feature acquisition submodule according to the second weighted average value; According to the third weighted average value, the exposure parameter and the light source intensity parameter of the iris feature acquisition submodule are adjusted.

4. The smart unlocking method based on biometric recognition and environmental perception as claimed in claim 1, characterized in that: The step of evaluating the security status of the environment around the smart door lock based on the environmental information to obtain a target security status includes: Performing standardization processing on the environmental information to obtain standardized environmental information; The safety-related features in the standardized environmental information are combined into an environmental feature vector, and the environmental feature vector is transmitted to a pre-trained random forest model to obtain the target safety state.

5. The smart unlocking method based on biometric recognition and environmental perception as claimed in claim 1, characterized in that: The detecting of abnormal user activity signs to obtain abnormal user activity sign detection results includes: Based on the environmental information, determine the approach speed, approach time period, and action posture of the user when approaching the smart door lock; The approach speed, approach time period, and action posture are compared with preset abnormal activity signs, and the abnormal activity sign detection result of the user is determined according to the abnormal activity sign comparison result.

6. The smart unlocking method based on biometric recognition and environmental perception as claimed in claim 1, characterized in that: The target security status includes safe, warning, and dangerous; the user abnormal activity sign detection result includes normal, suspicious, and abnormal; and the unlocking risk rating is determined according to the environmental perception result, including: When the target security state is safe and the detection result of the abnormal activity sign of the user is normal, determining that the unlocking risk rating is low risk; When the target security status is a warning, or the detection result of the abnormal activity sign of the user is suspicious, determining that the unlocking risk rating is a medium risk; When the target security state is dangerous and the detection result of the abnormal activity sign of the user is abnormal, the unlocking risk rating is determined to be high risk.

7. The smart unlocking method based on biometric recognition and environmental perception as claimed in claim 6, characterized in that: The determining of the intelligent unlocking condition according to the unlocking risk rating and the environment perception result includes: When the unlock risk rating is low risk, the target security state is safe, and the detection result of the abnormal activity sign of the user is normal, setting the smart unlock condition to unlocking by using the first biometric feature; When the unlock risk rating is medium risk, the target security status is warning, or the detection result of the user's abnormal activity sign is suspicious, the smart unlock condition is set to unlocking through the first biometric feature and the second biometric feature; When the unlocking risk rating is high risk, the target security status is dangerous, and the detection result of the user's abnormal activity signs is abnormal, the smart unlocking condition is set to prohibit unlocking through the first biometric feature and the second biometric feature, and unlocking is performed through the unlocking control program or key pre-installed on the user's mobile terminal.

8. An intelligent unlocking method and device based on biometric recognition and environmental perception, characterized in that: The device comprises: A data acquisition module, used to acquire environmental information around the smart door lock and a first biometric feature of a user to unlock the smart door lock; An environmental perception module is used to evaluate the security status of the environment around the smart door lock based on the environmental information to obtain a target security status, and to detect signs of abnormal user activities to obtain a detection result of abnormal user activities, and to integrate the target security status and the detection result of abnormal user activities into an environmental perception result; A risk rating module, used to determine an unlocking risk rating according to the environmental perception result, and to determine a smart unlocking condition according to the unlocking risk rating and the environmental perception result; The smart unlocking module is used to compare the first biometric feature with a pre-stored user feature template based on the smart unlocking condition, and unlock the smart door lock when the first biometric feature and the user feature template match, or to obtain a second biometric feature and unlock the smart door lock when the first biometric feature and the second biometric feature and the user feature template match.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein the computer program, when running, executes the smart unlocking method based on biometric recognition and environmental perception as described in any one of claims 1 to 7.

10. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to execute the smart unlocking method based on biometric recognition and environmental perception according to any one of claims 1 to 7 through the computer program.

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