Smart home door lock and use method

Abstract

The invention provides a smart home door lock and a use method thereof, and the method comprises the steps: responding to a door lock operation request, executing multi-protocol network detection, and determining an online or offline mode according to a detection result, the network detection comprising state detection of a wireless communication module and network stability calculation; a cloud or local password generation mode is selected based on a network state, the generated password is stored in a secure storage medium, and local password generation relates to combined processing of a device key and environment parameters; multi-mode biological recognition is started after password verification succeeds, at least two biological characteristics are fused for composite verification, and biological characteristic verification comprises characteristic extraction and fusion comparison. The intelligent door lock has the beneficial effects that the reliability, the safety and the cruising ability of equipment in a complex environment are remarkably improved while the convenience of the intelligent door lock is kept, and the intelligent door lock is suitable for various application scenes such as families and office places.

Description

Technical Field

[0001] The present invention belongs to the technical field related to smart locks, and in particular relates to a smart home door lock and a method of using the same. Background Art

[0002] With the popularization of smart home technology, smart door locks have gradually replaced traditional mechanical locks and become an important device for home security. Existing smart door locks generally use password verification, biometric recognition or mobile remote control to achieve unlocking functions, but in actual use, there are still the following problems:

[0003] Strong network dependency: Most smart door locks rely on cloud servers to generate dynamic passwords. When the network signal is unstable or completely disconnected, users may not be able to obtain valid passwords, resulting in limited door lock functionality. Although some products support offline mode, the generated passwords lack a dynamic change mechanism, which poses a risk of being cracked.

[0004] Insufficient biometric reliability: Single biometric features (such as fingerprints or faces) are easily affected by environmental factors. For example, fingerprint sensors have lower recognition rates when fingers are wet, while facial recognition tends to fail in dimly lit scenes. Furthermore, traditional biometric matching methods are less adaptable to feature changes.

[0005] Extensive power consumption management: The continuous operation of the wireless communication module (such as Wi-Fi and Bluetooth) of the smart door lock will quickly consume battery power. Existing power-saving solutions mostly wake up at fixed time intervals and cannot be dynamically adjusted according to the user's actual usage habits, resulting in shortened battery life or delayed response.

[0006] Passive security: Abnormal state handling relies heavily on remote alarm notifications, leaving local protection measures weak. When subjected to brute force attacks or physical disassembly, the door lock cannot quickly shut down critical functions, and temporary password data can be maliciously read. Summary of the Invention

[0007] In view of this, the present invention aims to provide a smart home door lock and a method of use to at least solve one problem in the background technology.

[0008] To achieve the above object, the technical solution of the present invention is achieved as follows:

[0009] How to use smart home door locks, including:

[0010] Step S101, network status verification: In response to receiving a door lock operation request, performing multi-protocol network detection, the multi-protocol network detection includes: detecting the response status of the gateway device through the LoRa communication module; measuring the signal strength attenuation gradient through the BLE module; calculating the network connection stability coefficient based on the TCP three-way handshake delay, and determining that the network is in online mode when the stability coefficient is greater than a preset threshold; otherwise, determining that the network is in offline mode;

[0011] Step S102, password generation and storage: When in online mode, a password generation request including a device unique identifier and a synchronization timestamp is sent to a cloud server, and a dynamic password sequence generated by the cloud based on the SHA-256 algorithm is received. When in offline mode, a local password generation protocol is executed: the device root key is extracted from the security chip, the current system time is divided into preset time windows, a reference time identifier is generated, the root key is XORed with the reference time identifier to generate intermediate encrypted data; a cyclic shift operation is performed on the intermediate encrypted data, and the number of shift bits is dynamically determined by the last two digits of the temperature sensor real-time data and the pressure sensor data;

[0012] Step S103, multimodal biometric verification: After the password is verified, the biometric feature acquisition module is activated: a fingerprint image is acquired through a capacitive fingerprint sensor, and the spatial coordinates of at least 32 feature points are extracted; a near-infrared camera is used to capture the palm vein pattern and generate a vein topology map; the spatial coordinates and the vein topology map are input into a feature fusion neural network, and a composite biometric feature vector is output; when the cosine similarity between the composite biometric feature vector and a pre-stored template is greater than a preset value, an unlock instruction is generated;

[0013] Step S104, dynamic power consumption management: Real-time monitoring of the voltage change rate of the power module. When the voltage drop rate is detected to exceed 0.5mV / s, the power supply circuit of the face recognition module is shut down and the Wi-Fi module is switched to a periodic wake-up mode. The wake-up period is dynamically adjusted according to an autoregressive model based on historical usage records. The clock frequency of the main control chip is graded and reduced to maintain full speed of the real-time clock module.

[0014] Step S105, abnormal status processing: when the number of consecutive verification failures reaches a preset number, activate the three-axis accelerometer to collect vibration signals, perform wavelet packet transform on the signal to extract the 2-5kHz frequency band energy, and if the frequency band energy value exceeds the preset value of the historical benchmark value, trigger the sound and light alarm device, and send an encrypted alarm message to the preset security terminal; erase the temporary password data in the flash memory chip, and disable the password input function through the hardware fuse mechanism.

[0015] Furthermore, the method for determining the number of shift bits in the cyclic shift operation includes:

[0016] Read the current temperature value output by the temperature sensor and extract the last two significant digits as the first base number;

[0017] Read the instantaneous air pressure value output by the air pressure sensor and extract the third digit after the decimal point as the second base number;

[0018] It is calculated by the formula: number of shift bits = (first base number × second base number) mod 7 + 1; where mod represents a modulo operation, and the calculation process is completed in the isolated execution environment of the security chip.

[0019] Furthermore, the feature fusion neural network includes:

[0020] An input layer configured to receive a 128-dimensional fingerprint feature vector and a 128-dimensional vein feature vector;

[0021] Feature cross attention layer, calculates the weight distribution coefficient of the two types of features:

[0022] α = σ(W_f·f+W_v·v+b), where σ represents the Sigmoid function, W_f and W_v are trainable weight matrices, b is the bias term, and f and v are the fingerprint and vein feature vectors, respectively;

[0023] The output layer is fused to generate a 256-dimensional composite feature vector: F = α·f + (1-α)·v, and the weight distribution coefficient α is obtained by training with 500,000 sets of biometric data.

[0024] Furthermore, the adaptive adjustment method of the periodic wake-up mode includes:

[0025] Count the timestamp sequence {t1, t2, ..., t n}; Calculate the standard deviation σ of the time interval between adjacent operations: σ=√[Σ(t i+1 -t i -μ) 2 / (n-1)]; when σ<30 minutes, set the fixed wake-up period to 5 minutes; when σ≥30 minutes, establish the probability density function of the time interval and set the wake-up period to: T=max(10 minutes,60 minutes×e^(-kσ)); where k is the attenuation coefficient, ranging from 0.01 to 0.05.

[0026] Furthermore, the method for generating the encrypted alarm message includes: using the SM4 national encryption algorithm to encrypt the following data: the WGS84 code value of the current GPS coordinate, the SHA-256 hash value of the door lock's unique identifier, the wavelet packet energy spectrum of the abnormal vibration signal, and the thumbnail of the environmental photo collected in real time. The encrypted data is encapsulated into a TLV format message and sent to the security server via the LoRaWAN protocol.

[0027] Furthermore, the present solution discloses a smart home door lock, comprising: a main control module, including an embedded microprocessor and an independent clock unit, the embedded microprocessor being configured to coordinate the timing control of each module, and the independent clock unit having a built-in temperature compensation mechanism; a multi-protocol communication module, including a first wireless communication unit and a second short-range communication unit, the first wireless communication unit being configured to perform long-distance data transmission in a first preset frequency band, the second short-range communication unit being configured to perform near-field interaction in a second preset frequency band, and an electromagnetic isolation structure being provided between the first preset frequency band and the second preset frequency band; a biometric feature acquisition module, including a fingerprint sensing unit and a vein imaging unit, the fingerprint sensing unit being configured to acquire epidermal texture features, the vein imaging unit including a near-infrared light source and an optical sensor, the near-infrared light source emitting a light beam of a specific wavelength to penetrate biological tissue, and the optical sensor being configured to capture an image of subcutaneous blood vessel distribution; a dynamic password generation module, including a security encryption chip and a password generator. The password generation engine, the security encryption chip stores the unique identification information of the device, and the password generation engine is configured to switch between the online encryption protocol and the offline encryption protocol according to the network status; the power management module includes a multi-way power supply circuit and a power consumption control unit, the multi-way power supply circuit supports automatic switching between the main power supply and the backup power supply, and the power consumption control unit is configured to dynamically allocate the power supply priority of each module according to the working status of the door lock; the abnormal response module includes a vibration detection unit and a physical protection unit, the vibration detection unit is configured to analyze the mechanical impact signal characteristics, and the physical protection unit is configured to trigger the hardware-level protection mechanism when a preset threat mode is detected; the main control module establishes a control channel with the multi-protocol communication module through the first bus, and establishes a data channel with the biometric feature acquisition module through the second bus. A dedicated encrypted data interface is provided between the dynamic password generation module and the main control module, and the control signal output end of the power management module is connected to the power input end of each functional module.

[0028] Furthermore, the electromagnetic isolation structure includes: a bandpass filter component, configured to block signal crosstalk between the first preset frequency band and the second preset frequency band; a shielding shell, which uses a high magnetic permeability material to form a closed cavity, and the interior of the closed cavity is divided into independent radio frequency chambers; the fingerprint sensing unit includes an adaptive sensitivity adjustment circuit, configured to dynamically adjust the sensing threshold according to environmental parameters; the optical path system of the venous imaging unit includes a lens group and a filter, configured to enhance the contrast of vascular features.

[0029] Furthermore, the offline encryption protocol of the dynamic password generation module includes: a time synchronization unit, configured to generate a reference time identifier; a key processing unit, configured to perform nonlinear operations on the device's unique identification information and the reference time identifier; a displacement encoding unit, configured to dynamically adjust encoding parameters based on environmental sensor data; the hardware-level protection mechanism includes: a circuit fuse component, connected in series in the power supply circuit of the password input interface; and a data erasure circuit, configured to clear the temporary key in the non-volatile memory when a physical disassembly signal is detected.

[0030] Compared with the prior art, the smart home door lock and the method of use of the present invention have the following beneficial effects:

[0031] (1) The smart home door lock and its use method described in the present invention ensure security through a cloud-based dynamic password in online mode, and generate a dynamic password in offline mode by combining the device root key and environmental parameters, effectively avoiding the risk of cracking caused by a fixed password when the network is disconnected. At the same time, the real-time data of the temperature and air pressure sensors are used to participate in the password generation, so that the offline password changes dynamically with the environment, making it difficult for attackers to crack it through fixed patterns.

[0032] (2) The smart home door lock and its use method described in the present invention integrate the spatial coordinates of fingerprints and the topological features of palm veins, dynamically assign weights through a cross-attention mechanism, and reduce the impact of failure of a single biometric feature. For example, when a fingerprint is partially distorted by stains, the weight of the vein feature is automatically increased to maintain the overall recognition accuracy. At the same time, the neural network is trained using large-scale biometric data, significantly improving the adaptability to changes in individual features, such as slight wear of the fingerprint or changes in the vein with age.

[0033] (3) The smart home door lock and its use method described in the present invention dynamically shuts down non-essential functional modules (such as face recognition) based on the voltage change rate to avoid system downtime caused by sudden power drops. The wake-up cycle is predicted based on historical usage data. For example, the sleep time of the Wi-Fi module is automatically extended during low-frequency periods at night, thus balancing power saving and response speed.

[0034] (4) The smart home door lock and its use method described in the present invention distinguish normal operation from abnormal impact through vibration signal frequency band analysis, thereby reducing the false alarm rate; the hardware-level fuse mechanism directly cuts off the power supply to the password input interface, which is more difficult to circumvent than software locks, and simultaneously erases non-volatile memory data to prevent information leakage;

[0035] (5) The smart home door lock and its use method described in the present invention have an electromagnetic isolation design that effectively avoids interference from multi-band communication signals, ensuring the parallel stability of LoRa long-distance communication and BLE near-field interaction. At the same time, the temperature compensation mechanism of the independent clock module ensures the time synchronization accuracy in offline mode, avoiding password generation errors caused by changes in ambient temperature. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The accompanying drawings, which constitute part of the present invention, are provided to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are provided to explain the present invention and do not constitute an undue limitation of the present invention. In the accompanying drawings:

[0037] Figure 1 This is a schematic diagram of a method for using the smart home door lock according to an embodiment of the present invention. DETAILED DESCRIPTION

[0038] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0039] The present invention will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0040] This solution discloses a smart home door lock and its use method. The specific implementation process is as follows:

[0041] How to use smart home door locks, including:

[0042] Step S101, network status verification: In response to receiving a door lock operation request, perform multi-protocol network detection, which includes: detecting the response status of the gateway device through the LoRa communication module, specifically sending a detection data packet containing a timestamp to the preset gateway during implementation, and determining that the gateway is offline if no response is received within 500ms; measuring the signal strength attenuation gradient through the BLE module, recording the three most recent signal strength values ​​and calculating the attenuation slope per unit time during implementation; calculating the network connection stability coefficient based on the TCP three-way handshake delay, specifically calculating the stability coefficient by the ratio of the standard deviation of the consecutive three-way handshake delay to the average value. When the stability coefficient is greater than a preset threshold, it is determined to be in online mode; otherwise, it is determined to be in offline mode;

[0043] Step S102, password generation and storage: When in online mode, a password generation request including a device unique identifier and a synchronization timestamp is sent to a cloud server, and a dynamic password sequence generated by the cloud based on the SHA-256 algorithm is received, where the timestamp accuracy is controlled within the range of ±1 second; when in offline mode, a local password generation protocol is executed: the device root key is extracted from the security chip, the current system time is divided into a preset time window, and a reference time identifier is generated. During implementation, the system time is converted into a UNIX timestamp and rounded according to a 30-minute window; the root key is XORed with the reference time identifier to generate intermediate encrypted data; a cyclic shift operation is performed on the intermediate encrypted data, and the number of shifted bits is dynamically determined by the last two digits of the real-time data of the temperature sensor and the data of the pressure sensor. In specific implementation, the last two digits of the temperature value are processed as absolute values, and the third digit after the decimal point of the pressure data is obtained by rounding;

[0044] Step S103, multimodal biometric verification: After the password verification is passed, the biometric feature acquisition module is activated: a fingerprint image is acquired through a capacitive fingerprint sensor, and the spatial coordinates of at least 32 feature points are extracted. The feature points are located using the Harris corner detection algorithm; a near-infrared camera is used to capture the palm vein pattern and generate a vein topology diagram. Specifically, a vascular branch diagram is generated through binarization processing and a skeleton extraction algorithm; the spatial coordinates and the vein topology diagram are input into a feature fusion neural network, and a composite biometric feature vector is output. The feature vector is normalized using a min-max normalization method; when the cosine similarity between the composite biometric feature vector and a pre-stored template is greater than a preset value, an unlock instruction is generated;

[0045] Step S104, dynamic power consumption management: Real-time monitoring of the voltage change rate of the power module. When the voltage drop rate is detected to exceed 0.5mV / s, the power supply circuit of the face recognition module is shut down, specifically by cutting off the power supply loop through the MOS tube; the Wi-Fi module is switched to periodic wake-up mode, and the wake-up period is dynamically adjusted according to the autoregressive model of historical usage records. During implementation, an ARIMA model is established to predict the usage frequency within the next two hours; the clock frequency of the main control chip is graded and reduced to maintain the full speed of the real-time clock module. Specifically, the clock division coefficient is adjusted through the PLL phase-locked loop;

[0046] Step S105, abnormal state processing: When the number of consecutive verification failures reaches a preset number, the three-axis accelerometer is activated to collect vibration signals, and the signal is subjected to wavelet packet transform to extract the energy of the 2-5kHz frequency band. Specifically, a 5-layer wavelet packet decomposition tree is used to divide the frequency band; if the energy value of the frequency band exceeds the preset value of the historical benchmark value, the sound and light alarm device is triggered, and an encrypted alarm message is sent to the preset security terminal, wherein the alarm sound pressure level reaches 90dB or above; the temporary password data in the flash memory chip is erased, and the password input function is disabled through the hardware fuse mechanism, and the fuse current threshold is set to 500mA;

[0047] Furthermore, the method for determining the number of shift bits in the cyclic shift operation includes:

[0048] Read the current temperature value output by the temperature sensor, extract the last two significant digits as the first base number, and take the absolute value of the last two digits as "53" when the temperature is -5.3°C;

[0049] Read the instantaneous pressure value output by the pressure sensor, extract the third digit after the decimal point as the second base number. For a pressure value of 1013.25hPa, take the third digit after the decimal point as "3";

[0050] The number of shifted bits is calculated using the formula: (first base number × second base number) mod 7 + 1. For example, (53 × 3) = 159, 159 mod 7 = 5, and the final number of shifted bits = 5 + 1 = 6. Where mod represents a modulo operation. The calculation process is completed in the isolated execution environment of the security chip.

[0051] Furthermore, the feature fusion neural network includes:

[0052] The input layer is configured to receive 128-dimensional fingerprint feature vectors and 128-dimensional vein feature vectors. The input data is processed by the Batch Normalization layer;

[0053] The feature cross-attention layer calculates the weight distribution coefficient of the two types of features: α = σ(W_f·f+W_v·v+b), where the weight matrices W_f and W_v are of dimension 128×128 and the bias term b is initialized to 0.5. The fusion output layer generates a 256-dimensional composite feature vector: F = α·f+(1-α)·v. The weight distribution coefficient α is obtained by training on 500,000 sets of biometric data using the cross-entropy loss function and the Adam optimizer.

[0054] Furthermore, the adaptive adjustment method of the periodic wake-up mode includes:

[0055] Count the timestamp sequence {t1, t2, ..., t n}, timestamp parsing is accurate to the second level;

[0056] Calculate the standard deviation σ of the time interval between adjacent operations: σ=√[Σ(t i+1 -t i -μ) 2 / (n-1)], where μ is the mean time interval. When σ<30 minutes, a fixed wake-up period of 5 minutes is set. When σ≥30 minutes, a probability density function of the time interval is established, and the wake-up period is set to: T = max(10 minutes, 60 minutes × e^(-kσ)), where the attenuation coefficient k is determined in the range of 0.01-0.05 by the grid search method.

[0057] Furthermore, the method for generating the encrypted alarm message includes:

[0058] The following data is encrypted using the SM4 national encryption algorithm: the WGS84 encoded value of the current GPS coordinates (with six decimal places for longitude and latitude), the SHA-256 hash value of the door lock's unique identifier (1000 hash iterations), the wavelet packet energy spectrum of the abnormal vibration signal (retaining the first 128 frequency points), and thumbnails of real-time environmental photos (compressed into 160×120 pixel JPEG format).

[0059] The encrypted data is encapsulated into a TLV format message with the type field identified as 0x01 Emergency Alarm and the length field containing the 4-byte data length. It is sent to the security server via the LoRaWAN protocol, with the sending interval set to 3 retransmissions per second.

[0060] Furthermore, the offline encryption protocol of the dynamic password generation module includes:

[0061] The time synchronization unit generates a reference time identifier and calibrates it with the local RTC clock through the NTP protocol;

[0062] The key processing unit performs a nonlinear operation on the device unique identification information and the reference time identifier, specifically using the HMAC-SHA1 algorithm to generate a 256-bit intermediate key;

[0063] The displacement encoding unit dynamically adjusts the encoding parameters according to the environmental sensor data. The temperature sensor data is used to generate the S-box permutation table, and the air pressure data is used to initialize the permutation seed.

[0064] Furthermore, the hardware-level protection mechanism includes:

[0065] The circuit fuse component is connected in series in the power supply circuit of the password input interface. It uses a recoverable fuse and requires a physical button to reset after being triggered.

[0066] When the data erasure circuit detects a physical removal signal, it sends a high-level pulse of 10ms to the CE pin of the flash memory chip to clear the temporary key in the non-volatile memory.

[0067] It should be noted that the specific hardware structures and circuits involved in the smart home door lock mentioned in this solution, unless specifically mentioned, are deemed to be achievable using existing technologies and will not be elaborated in this solution.

[0068] Those skilled in the art will appreciate that the units and method steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the components and steps of each example according to their functions. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0069] In the several embodiments provided in this application, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the division of the units described above is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. The above-mentioned units may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiment of the present invention.

[0070] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some or all of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and description of the present invention.

[0071] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. The method of using the smart home door lock is characterized by: include: Network status determination: Respond to door lock operation requests, perform multi-protocol network detection, and determine whether the mode is online or offline based on the detection results. The network detection includes status detection of the wireless communication module and network stability calculation; Dynamic password processing: Select cloud or local password generation based on network status, and store the generated password in a secure storage medium. Local password generation involves combining device keys with environmental parameters. Biometric verification: After the password verification is successful, multimodal biometric recognition is initiated, fusing at least two biometric features for composite verification. Biometric verification includes feature extraction and fusion comparison; Dynamic power consumption control: Real-time monitoring of power status and dynamic adjustment of the power supply strategy of functional modules based on voltage changes. The power supply strategy includes module power supply priority adjustment and wake-up cycle control. Security protection response: When continuous verification fails, the exception handling mechanism is triggered, environmental parameters are collected, and security protection operations are performed. The security protection operations include alarm triggering and hardware protection mechanism linkage.

2. The method for using the smart home door lock according to claim 1, characterized in that: The multi-protocol network detection specifically includes: Detect the gateway response status through the LoRa communication module; Measure the signal strength attenuation gradient through the BLE module; The network connection stability coefficient is calculated based on the TCP three-way handshake delay. If the stability coefficient is greater than the preset threshold, the network connection is considered to be in online mode; otherwise, the network connection is considered to be in offline mode.

3. The method for using the smart home door lock according to claim 1, characterized in that: The local password generation method includes: Extract the device root key from the security chip; Divide the current system time into a preset time window to generate a reference time identifier; Performing an XOR operation on the root key and the time identifier to generate intermediate encrypted data; determining the displacement parameter based on the real-time data of the temperature sensor and the air pressure sensor; Perform a circular shift operation on the intermediate encrypted data with a dynamic shift number.

4. The method for using the smart home door lock according to claim 1, characterized in that: The biometric verification specifically includes: Acquire a fingerprint image through a capacitive fingerprint sensor and extract spatial coordinates of at least 32 feature points; The palm vein patterns are collected by a near-infrared camera to generate a vein topology map; The two biometric input features are fused into a neural network to generate a composite biometric vector; When the cosine similarity between the composite feature vector and the pre-stored template is greater than a preset value, an unlocking instruction is generated.

5. The method for using the smart home door lock according to claim 1, characterized in that: The power supply strategy adjustment includes: When the voltage drop rate is detected to be greater than 0.5mV / s, the power supply to the face recognition module is turned off; Switch the Wi-Fi module to periodic wake-up mode, where the wake-up period is adjusted based on an autoregressive model based on historical usage records. The clock frequency of the main control chip is reduced in stages to keep the real-time clock module running at full speed.

6. The method for using the smart home door lock according to claim 1, characterized in that: The safety protection operation includes: Activate the accelerometer to collect vibration signals and extract energy in the 2-5kHz frequency band; When the energy value exceeds the historical benchmark, an audible and visual alarm is triggered and an encrypted alarm message is sent; Erases temporary password data in flash memory and disables password entry via hardware fuses.

7. A smart home door lock, a method for using a smart home door lock according to any one of claims 1 to 6, characterized in that: include: A main control module, comprising an embedded microprocessor and an independent clock unit, wherein the embedded microprocessor is configured to coordinate the timing control of each module, and the independent clock unit has a built-in temperature compensation mechanism; A multi-protocol communication module, comprising a first wireless communication unit and a second short-range communication unit, wherein the first wireless communication unit is configured to perform long-distance data transmission in a first preset frequency band, and the second short-range communication unit is configured to perform near-field interaction in a second preset frequency band, and an electromagnetic isolation structure is provided between the first preset frequency band and the second preset frequency band; A biometric feature acquisition module comprising a fingerprint sensing unit and a venous imaging unit, wherein the fingerprint sensing unit is configured to acquire epidermal line features, the venous imaging unit comprises a near-infrared light source and an optical sensor, wherein the near-infrared light source emits a light beam of a specific wavelength to penetrate biological tissue, and the optical sensor is configured to capture an image of subcutaneous blood vessel distribution; A dynamic password generation module, comprising a secure encryption chip storing unique device identification information and a password generation engine configured to switch between online and offline encryption protocols based on network status; A power management module, comprising a multi-way power supply circuit and a power consumption control unit. The multi-way power supply circuit supports automatic switching between the main power supply and the backup power supply. The power consumption control unit is configured to dynamically assign power supply priorities to each module based on the operating status of the door lock; an abnormality response module, comprising a vibration detection unit and a physical protection unit, wherein the vibration detection unit is configured to analyze mechanical shock signal characteristics, and the physical protection unit is configured to trigger a hardware-level protection mechanism when a preset threat pattern is detected; The main control module establishes a control channel with the multi-protocol communication module through the first bus, and establishes a data channel with the biometric acquisition module through the second bus. A dedicated encrypted data interface is provided between the dynamic password generation module and the main control module, and the control signal output end of the power management module is connected to the power input end of each functional module.

8. The smart home door lock according to claim 7, characterized in that: The electromagnetic isolation structure comprises: a bandpass filter component configured to block signal crosstalk between a first preset frequency band and a second preset frequency band; The shielding shell is formed of a closed cavity using a high magnetic permeability material, and the interior of the closed cavity is divided into independent radio frequency chambers; The fingerprint sensing unit includes an adaptive sensitivity adjustment circuit configured to dynamically adjust the sensing threshold according to environmental parameters; The optical path system of the venous imaging unit includes a lens group and a filter, which is configured to enhance the contrast of blood vessel features.

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