Intelligent door lock safety management and control system based on Internet of Things

Abstract

The invention discloses an intelligent door lock safety management and control system based on the Internet of Things, and relates to the technical field of door lock management and control. A sliding window is adopted to process data, and improved adaptive median filtering is used for noise reduction; environment parameter dynamic features are extracted, and a three-dimensional environment feature vector is constructed; handle pressure distribution is collected, the angle change rate and complexity are calculated, and behavior feature vectors are constructed; generating a 128-bit dynamic key in combination with environment and behavior characteristics; and mixing the dynamic key and the master key, encrypting and transmitting to the cloud for password authentication and instruction updating. Multi-dimensional environment parameters are collected in real time, space-time association fusion is carried out on the multi-dimensional environment parameters and user operation behaviors, a dynamic secret key is generated, it is ensured that each authentication process is bound with the current environment and user operation characteristics, the secret key cannot be predicted or copied through historical data, and the defense capacity for man-in-the-middle attacks and data stealing is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of door lock management and control, and in particular relates to an intelligent door lock security management and control system based on the Internet of Things. Background Art

[0002] With the booming development of IoT technology, smart door locks have gained widespread adoption in homes and offices due to their convenience and intelligent features. Existing IoT smart door locks generally use static authentication mechanisms (such as fixed passwords, biometric templates, or preset keys). Their core flaw lies in the decoupling of authentication factors from the physical environment. An attacker can intercept a legitimate user's authentication data packets (such as encryption keys or fingerprint signatures in Bluetooth communications) and launch a replay attack at the same physical location.

[0003] Existing dynamic key schemes (such as time-based OTP) rely only on the internal state of the device. When an attacker replays legitimate user operations at different locations and times, the system cannot identify abnormal switching of environmental scenarios, rendering coarse-grained protection measures such as geo-fencing ineffective. To address the above problems, the following solution is proposed. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent door lock security management and control system based on the Internet of Things. By collecting multi-dimensional environmental parameters in real time and integrating them with user operation behaviors in time and space, a dynamic key is generated to ensure that each authentication process is deeply bound to the current physical environment and user operation characteristics, solving the problem that the existing system cannot recognize abnormal switching of environmental scenes, making coarse-grained protection measures such as geographic fences ineffective.

[0005] To solve the above technical problems, the present invention is achieved through the following technical solutions:

[0006] The present invention is an intelligent door lock security management and control system based on the Internet of Things. The management and control system includes a local perception layer, an edge computing layer, a cloud service layer, and a user interaction layer. The local perception layer includes a multi-source sensor module, an environment synchronization acquisition unit, a behavior acquisition unit, an environment preprocessing module, and a behavior preprocessing module.

[0007] The edge computing layer includes a spatiotemporal feature extraction module, a behavior feature modeling module, a dual-stream fusion module, and a dynamic key generation module;

[0008] The cloud server includes a secure transmission module, an authentication decision module, an environmental feature database and a key management module;

[0009] The user interaction layer includes a handle pressure array and a user terminal APP;

[0010] The workflow of the control system is as follows:

[0011] Step S1, multi-source synchronous acquisition: integrating magnetic, light-sensitive, and air pressure sensors to synchronously acquire environmental data through a time protocol;

[0012] Step S2, dynamic preprocessing: Segment the data using a sliding window and use an adaptive median filter algorithm to eliminate environmental noise interference;

[0013] Step S3, spatiotemporal feature construction: extracting magnetic field gradient tensor, illumination fluctuation rate, and air pressure trend coefficient to form a three-dimensional environment feature vector;

[0014] Step S4, behavioral feature extraction: analyzing the handle pressure distribution, calculating the pressure center offset, angle change rate, and operation entropy value;

[0015] Step S5, dual-stream key generation: Generate a 128-bit dynamic key by fusing environmental and behavioral features through weighted matrix transformation and lightweight neural network;

[0016] Step S6, cloud-based dual authentication: After XORing the master key, the SM9 encryption protocol is used to perform interactive verification of time-sensitive data with the cloud.

[0017] Furthermore, the output end of the multi-source sensor module is unidirectionally connected to the environment synchronization acquisition unit, the output end of the environment synchronization acquisition unit is unidirectionally connected to the environment preprocessing module, and the output end of the behavior acquisition unit is unidirectionally connected to the behavior preprocessing module;

[0018] The output end of the environment preprocessing module is unidirectionally connected to the spatiotemporal feature extraction module, the output end of the spatiotemporal feature extraction module is unidirectionally connected to the dual-stream fusion module, the output end of the behavior preprocessing module is unidirectionally connected to the behavior feature modeling module, the output end of the behavior feature modeling module is unidirectionally connected to the dual-stream fusion module, and the output end of the dual-stream fusion module is connected to the dynamic key generation module;

[0019] The output end of the dynamic key generation module is unidirectionally connected to the secure transmission module, the output end of the secure transmission module is unidirectionally connected to the authentication decision module, and the output end of the authentication decision module is unidirectionally connected to the environmental feature database and the key management module respectively;

[0020] The handle pressure array output end is unidirectionally connected to the behavior acquisition unit, and the user terminal APP is bidirectionally connected to the authentication decision module.

[0021] Furthermore, the multi-source sensor module integrates a magnetometer, a photosensor, and a barometer to collect the magnetic field intensity, light intensity, and air pressure value around the door lock in real time;

[0022] The environmental synchronization acquisition unit is used to achieve timestamp alignment of multi-sensor data through the IEEE 1588 protocol, detect abnormal mutations in magnetic field, light, and air pressure, and filter interference signals that exceed the baseline threshold;

[0023] The handle pressure array is an 8×8 piezoelectric sensor matrix integrated into the door lock handle. It is used to detect the distribution and dynamic changes of hand pressure during user operation in real time. It generates operation trajectory data through spatial pressure density mapping, providing raw input signals for behavioral feature modeling and capturing the user's unique force application habits.

[0024] The behavior acquisition unit is used to analyze the distribution characteristics of the 8×8 pressure matrix, track the movement trajectory of the pressure center during handle operation, and generate raw behavior data including spatial offset and temporal complexity;

[0025] The environmental preprocessing module is used to reduce noise on the original environmental data by using a sliding window and an improved median filter algorithm, and calculate dynamic features such as the magnetic field gradient tensor, illumination fluctuation rate, and air pressure trend coefficient;

[0026] The behavior preprocessing module is used to extract the angle change rate by calculating the centroid of the pressure distribution, and to construct a feature vector of the user behavior by combining the randomness of the operation sequence with the Shannon entropy quantification;

[0027] The spatiotemporal feature extraction module is used to extract dynamic features with spatiotemporal correlation from the preprocessed environmental data. By calculating the magnetic field gradient tensor, the light fluctuation rate and the air pressure trend coefficient, it constructs a feature vector reflecting the spatiotemporal variation of environmental parameters, providing a quantitative basis for the physical environment dimension for subsequent dynamic key generation.

[0028] The behavior feature modeling module is used to convert the original operation data collected by the handle pressure array into a behavior feature vector, calculate the angle change rate through the pressure center offset trajectory, and combine it with Shannon entropy analysis of the operation time sequence to establish a dynamic mathematical model of the user's operation behavior and quantify the uniqueness and randomness characteristics of the behavior pattern;

[0029] The dual-stream fusion module is used to process the environment and behavior feature streams respectively using a lightweight convolutional network, and realize cross-modal feature coupling through the Hadamard product operation of the cross-entropy fusion layer;

[0030] The dynamic key generation module is used to perform SHA-3 hash compression and truncation operations on the fusion feature to generate a 128-bit temporary key, and perform XOR confusion with the master key to enhance randomness;

[0031] The secure transmission module is used to encapsulate data frames based on the LoRaWAN protocol, encrypt dynamic keys using the SM9 algorithm, and attach a digital signature with a timestamp to prevent replay attacks;

[0032] The authentication decision module is used to verify the validity of the key time window, compare the historical patterns of the cloud environment feature database, decide whether to authorize unlocking and trigger an abnormal warning;

[0033] The environmental feature database is used to store and manage the temporal and spatial feature baseline data of historical environmental parameters, including the normal fluctuation range and correlation pattern of magnetic field, light, and air pressure in different time periods and geographical locations, providing a dynamic environmental feature comparison benchmark for cloud authentication decisions and identifying abnormal environmental interference or attack behaviors;

[0034] The key management module is used to dynamically update the master key seed, manage the key distribution and revocation of multiple devices, and record key generation logs for security audit tracing;

[0035] The user terminal APP is used to provide a human-computer interaction interface for unlocking status visualization, environmental abnormality alarm push, security policy configuration and operation history query.

[0036] Furthermore, the step S1, multi-source synchronous acquisition specifically includes the following steps:

[0037] Step S11: Integrate a three-axis magnetometer, a micro photosensor, and a MEMS barometer inside the door lock;

[0038] Step S12: Establish a timestamp synchronization mechanism, using the IEEE 1588 precision time protocol to ensure that the time error of the three sensor data collection is less than 1ms;

[0039] Step S13: Setting environmental parameter baseline thresholds, specifically: magnetic field intensity fluctuation threshold ΔM, illumination mutation threshold ΔL, and air pressure change rate threshold ΔP;

[0040] This design integrates a magnetometer, photosensor, and barometer to collect physical environmental parameters such as the magnetic field, light, and air pressure around the door lock in real time using a millisecond-level time synchronization mechanism. It also sets dynamic baseline thresholds to provide the raw data basis for subsequent analysis, ensuring the authenticity and timeliness of environmental characteristics.

[0041] Furthermore, the step S2, dynamic preprocessing specifically includes the following steps:

[0042] Step S21: Perform sliding window processing on the original data (window length T = 2s, step length Δt = 0.5s);

[0043] Step S22: Using an improved median filter algorithm to eliminate impulse noise and achieve adaptive noise suppression, specifically:

[0044] For the original data sequence X={x1,x2,…,x k}, output after filtering:

[0045]

[0046] Where y k is the output value after filtering, median(·) is the median of the data in the window, x k-2 is the k-2th sampling point in the original data sequence, μ is the arithmetic mean of the data in the sliding window, σ is the standard deviation of the data in the sliding window, and e is the base of the natural logarithm;

[0047] This design uses a sliding window and adaptive median filtering algorithm to eliminate impulse noise and instantaneous interference in sensor data, retain valid signals through dynamic weighting, improve the reliability and consistency of environmental parameters, and lay a high-quality data foundation for feature extraction.

[0048] Furthermore, the step S3, constructing the spatiotemporal features specifically includes the following steps:

[0049] Step S31: Extract the dynamic characteristics of the environmental parameters in the window, which are:

[0050] Magnetic field gradient tensor

[0051] Where M x ,M y ,M z All are measured values of the three-axis magnetometer, T is the vector transpose sign;

[0052] Light fluctuation rate

[0053] Where L is the sampling sequence of the photosensor in the window, t start ,t end The timestamps of the window start and end respectively;

[0054] Pressure trend coefficient

[0055] Where p i is the i-th air pressure sampling value, is the mean air pressure in the window, t i is the timestamp of the i-th sampling point, is the midpoint of the window time;

[0056] Step S32: The three characteristic parameters in step S31 are normalized and combined into a three-dimensional characteristic vector to form an environmental characteristic matrix containing spatiotemporal correlation characteristics, providing a quantitative basis for the physical environment dimension for dynamic key generation:

[0057] F env =[||G M ||2,R L ,|CP |] T ;

[0058] Where, F env is a three-dimensional eigenvector, ||G M ||2 is the L2 norm of the magnetic field gradient tensor, |C P | is the absolute value of the pressure trend coefficient;

[0059] This design extracts spatiotemporal dynamic features such as magnetic field gradient tensor, light fluctuation rate and air pressure trend coefficient from preprocessed data, fuses them into three-dimensional feature vectors, quantifies the spatiotemporal correlation of environmental parameters, and forms a feature expression that can characterize the physical state of the door lock's surroundings.

[0060] Furthermore, the step S4, extracting behavioral features, specifically includes the following steps:

[0061] Step S41: Using an 8×8 piezoelectric pressure sensor array (64 pressure units in total) deployed on the door lock handle, real-time pressure distribution data during user operation is captured;

[0062] Step S42: Calculate the operation characteristics of the handle respectively, specifically:

[0063] Center of pressure offset

[0064] Where x i ,y i is the position coordinate of the i-th unit in the 8×8 pressure sensor array, f i is the pressure value of the i-th unit;

[0065] Angle change rate

[0066] Where Δt is the sampling time interval, arctan(·) is the inverse tangent function, is the change in the pressure center coordinates between adjacent samplings;

[0067] Operation timing complexity

[0068] Where q i is the proportion of each pressure unit;

[0069] Step S43: α and H b Integrate into a two-dimensional behavioral feature vector F act =[α,H b ] T , providing user-specific behavior characterization parameters for subsequent dynamic key generation;

[0070] This design analyzes the handle pressure distribution, pressure center offset trajectory and operation timing complexity, extracts the angle change rate and information entropy characteristics of user operations, constructs behavioral feature vectors, characterizes user operation habits, and realizes digital representation of behavioral patterns.

[0071] Furthermore, the step S5, generating the dual-stream key specifically includes the following steps:

[0072] Step S51: Design a two-stream cross entropy fusion layer:

[0073]

[0074] Where K is the generated dynamic key, Φ(·) is the SHA-3 hash function, σ(·) is the Sigmoid activation function, and F env is the environmental feature vector, F act is the behavioral feature vector, W e is the environmental flow parameter matrix, W a is the behavior flow parameter matrix, is the Hadamard product;

[0075] Step S52: To optimize the feature expression capability, the environment stream uses a 1×3 convolution kernel to extract cross-dimensional correlation features, and then processes them through batch normalization and GeLU activation function in sequence;

[0076] The behavior stream captures the operation timing pattern through 1×2 convolution kernels, and combines layer normalization and ReLU activation to enhance generalization;

[0077] Step S53: Using the truncation function, extract the lower 128 bits of valid data from the hash output to generate the dynamic key K t =Truncate(K,128bits);

[0078] This design uses a dual-stream cross-entropy fusion algorithm to transform environmental features and behavioral features through lightweight neural networks, and then performs hash fusion and truncation processing to generate a 128-bit dynamic key, achieving deep coupling of environmental parameters and user behavior, and ensuring the unpredictability and uniqueness of the key.

[0079] Furthermore, the step S6, cloud-based dual authentication specifically includes the following steps:

[0080] Step S61: K t XOR confusion with the user's preset master key K0:

[0081]

[0082] Where K final is the final generated 128-bit dynamic authentication key, Bitwise XOR operation, mod 2 128 It is a modular operation;

[0083] Step S62: The obfuscated K final Combined with the current timestamp and device ID, the SM9 identification password is encrypted using the cloud public key and transmitted to the authentication server;

[0084] Step S63: After decryption on the cloud, the legitimacy of the time window is verified and compared with the historical behavior patterns in the environmental feature database. If the authentication is successful, the cloud sends dynamic control instructions to the door lock and synchronously updates the local key seed. If an abnormal environmental feature is detected (such as a sudden change in the magnetic field or an excessive operation entropy value), the security warning mechanism is triggered and the authentication request is frozen.

[0085] This design confuses the dynamic key with the preset master key, and then transmits it to the cloud based on the national encryption SM9 algorithm and LoRaWAN encryption. It completes timestamp verification and environmental feature comparison, and updates the local key seed through dynamic instructions to achieve active defense mechanism and key lifecycle management of end-cloud collaboration.

[0086] The present invention has the following beneficial effects:

[0087] 1. The present invention generates dynamic keys by collecting multi-dimensional environmental parameters in real time and integrating them with user operation behaviors in time and space. This ensures that each authentication process is deeply bound to the current physical environment and user operation characteristics, making the key impossible to predict or copy through historical data, thereby improving the defense capability against man-in-the-middle attacks and data theft. Attackers cannot carry out effective replay attacks by intercepting historical authentication data. Due to the instantaneous and random nature of environmental parameters, the key generation has strict spatiotemporal uniqueness. The broad spectrum of environmental parameters further increases the difficulty for attackers to forge complete environmental scenarios, solving the defect that static keys or single biometric features are easily copied.

[0088] 2. The present invention adopts a dual-stream cross-entropy fusion algorithm to perform nonlinear coupling on the environmental feature vector and the behavioral feature vector, and realizes cross-modal feature interaction through parameter matrix transformation and Hadamard product operation; the algorithm does not rely on the reliability of a single feature source, but reduces the risk of misauthentication through the collaborative verification of environmental physical signals and user behavior indicators; even if some sensors encounter interference, the features of the remaining dimensions can still be automatically adjusted through the weight matrix to maintain system robustness; this design provides redundant protection for identity authentication and has higher fault tolerance than single-factor authentication.

[0089] 3. The system of the present invention continuously collects environmental parameters during the normal authentication process and establishes a dynamic baseline model through sliding window analysis and adaptive filtering. When an environmental anomaly is detected, such as the magnetic field intensity continuously deviating from the baseline or the air pressure fluctuating unnaturally, but the authentication request has not yet been triggered, an encrypted early warning signal can be sent to the cloud in advance. This enables the system to switch from passive response to active defense, and is particularly suitable for detecting covert physical attacks against door locks such as electromagnetic injection and vacuum adsorption technology. The spatiotemporal correlation characteristics of environmental parameters and user behavior can also help distinguish between natural interference such as sudden changes in air pressure caused by thunderstorms and human attacks, avoiding false alarms and gaining a critical time window for security response.

[0090] 4. The environmental parameters of the present invention have the physical unclonability property, and attackers cannot remotely and accurately simulate the real-time physical state of the target scene; the dynamic key output by the dual-stream fusion algorithm is strongly correlated with the environmental characteristics. Even if an attacker hijacks the communication link, he cannot forge a valid key that conforms to the cloud-based environmental characteristic database; combined with the SM9 identification password to encrypt the transmitted data, the end-to-end confidentiality of the final key is ensured; the dual authentication mechanism bound in time and space constitutes in-depth defense, making it impossible for man-in-the-middle attacks to simultaneously break through the two barriers of physical environment simulation and cryptographic protection.

[0091] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0092] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0093] Figure 1 This is a framework diagram of an IoT-based smart door lock security management and control system of the present invention;

[0094] Figure 2 This is a flow chart of an IoT-based smart door lock security management and control system of the present invention. DETAILED DESCRIPTION

[0095] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0096] See also Figure 1As shown, the present invention is an intelligent door lock security management and control system based on the Internet of Things, including a local perception layer, an edge computing layer, a cloud service layer and a user interaction layer. The local perception layer includes a multi-source sensor module, an environment synchronization acquisition unit, a behavior acquisition unit, an environment preprocessing module, and a behavior preprocessing module;

[0097] The edge computing layer includes a spatiotemporal feature extraction module, a behavioral feature modeling module, a dual-stream fusion module, and a dynamic key generation module;

[0098] The cloud server includes a secure transmission module, an authentication decision module, an environmental feature database, and a key management module;

[0099] The user interaction layer includes the handle pressure array and the user terminal APP;

[0100] The output end of the multi-source sensor module is unidirectionally connected to the environment synchronization acquisition unit, the output end of the environment synchronization acquisition unit is unidirectionally connected to the environment preprocessing module, and the output end of the behavior acquisition unit is unidirectionally connected to the behavior preprocessing module;

[0101] The output end of the environment preprocessing module is unidirectionally connected to the spatiotemporal feature extraction module, the output end of the spatiotemporal feature extraction module is unidirectionally connected to the dual-stream fusion module, the output end of the behavior preprocessing module is unidirectionally connected to the behavior feature modeling module, the output end of the behavior feature modeling module is unidirectionally connected to the dual-stream fusion module, and the output end of the dual-stream fusion module is connected to the dynamic key generation module;

[0102] The output end of the dynamic key generation module is unidirectionally connected to the secure transmission module, the output end of the secure transmission module is unidirectionally connected to the authentication decision module, and the output end of the authentication decision module is unidirectionally connected to the environmental feature database and the key management module respectively;

[0103] The handle pressure array output end is unidirectionally connected to the behavior acquisition unit, and the user terminal APP is bidirectionally connected to the authentication decision module.

[0104] The multi-source sensor module integrates a magnetometer, a photosensor, and a barometer to collect real-time information about the magnetic field intensity, light intensity, and air pressure around the door lock.

[0105] The environmental synchronization acquisition unit is used to achieve timestamp alignment of multi-sensor data through the IEEE 1588 protocol, detect abnormal changes in magnetic field, light, and air pressure, and filter out interference signals that exceed the baseline threshold;

[0106] The handle pressure array is an 8×8 piezoelectric sensor matrix integrated into the door lock handle. It is used to detect the distribution and dynamic changes of hand pressure during user operation in real time. It generates operation trajectory data through spatial pressure density mapping, providing raw input signals for behavioral feature modeling and capturing the user's unique force application habits.

[0107] The behavior acquisition unit is used to analyze the distribution characteristics of the 8×8 pressure matrix, track the motion trajectory of the pressure center during handle operation, and generate raw behavior data including spatial offset and temporal complexity;

[0108] The environmental preprocessing module is used to reduce the noise of the original environmental data using a sliding window and improved median filter algorithm, and calculate dynamic features such as the magnetic field gradient tensor, light fluctuation rate, and air pressure trend coefficient;

[0109] The behavior preprocessing module is used to extract the angle change rate by calculating the centroid of the pressure distribution, and combines the Shannon entropy to quantify the randomness of the operation sequence to construct the feature vector of the user behavior;

[0110] The spatiotemporal feature extraction module is used to extract dynamic features with spatiotemporal correlation from preprocessed environmental data. By calculating the magnetic field gradient tensor, light fluctuation rate, and air pressure trend coefficient, it constructs a feature vector that reflects the spatiotemporal variation of environmental parameters, providing a quantitative basis for the physical environment dimension for subsequent dynamic key generation.

[0111] The behavioral feature modeling module converts the raw operation data collected by the handle pressure array into a behavioral feature vector. It calculates the angle change rate through the pressure center offset trajectory and combines it with Shannon entropy analysis of the operation time sequence to establish a dynamic mathematical model of the user's operation behavior and quantify the uniqueness and randomness of the behavior pattern.

[0112] The dual-stream fusion module is used to process the environment and behavior feature streams separately using a lightweight convolutional network, and achieve cross-modal feature coupling through the Hadamard product operation of the cross-entropy fusion layer;

[0113] The dynamic key generation module is used to perform SHA-3 hash compression and truncation operations on the fused features to generate a 128-bit temporary key, which is then XOR-ed with the master key to enhance randomness.

[0114] The secure transmission module is used to encapsulate data frames based on the LoRaWAN protocol, encrypt dynamic keys using the SM9 algorithm, and attach a digital signature with a timestamp to prevent replay attacks;

[0115] The authentication decision module verifies the validity of the key time window, compares historical patterns in the cloud environment feature database, and decides whether to authorize unlocking and trigger an abnormality warning.

[0116] The environmental feature database is used to store and manage the temporal and spatial feature baseline data of historical environmental parameters, including the normal fluctuation ranges and correlation patterns of magnetic fields, illumination, and air pressure in different time periods and geographical locations. This provides a dynamic environmental feature comparison benchmark for cloud-based authentication decisions and helps identify abnormal environmental interference or attack behaviors.

[0117] The key management module is used to dynamically update the master key seed, manage key distribution and revocation for multiple devices, and record key generation logs for security audit tracing;

[0118] The user terminal APP is used to provide a human-computer interaction interface for unlocking status visualization, environmental abnormality alarm push, security policy configuration and operation history query.

[0119] See also Figure 2 As shown in the figure, the workflow of the control system is as follows:

[0120] Step S1, multi-source synchronous acquisition: Three types of sensors, a three-axis magnetometer, a micro photosensor, and a MEMS barometer, are precisely integrated inside the smart door lock. The sensors continuously collect raw data on the magnetic field intensity, light intensity, and air pressure values of the door lock's surrounding environment under a synchronized time mechanism. At the same time, baseline fluctuation thresholds for each environmental parameter are set as the basis for subsequent judgment of environmental stability. Specifically,

[0121] Step S11: Integrate a three-axis magnetometer, a micro photosensor, and a MEMS barometer inside the door lock;

[0122] Step S12: Establish a timestamp synchronization mechanism, using the IEEE 1588 precision time protocol to ensure that the time error of the three sensor data collection is less than 1ms;

[0123] Step S13: setting environmental parameter baseline thresholds, specifically: magnetic field intensity fluctuation threshold ΔM, illumination mutation threshold ΔL, and air pressure change rate threshold ΔP.

[0124] Step S2, dynamic preprocessing: The raw environmental sensor data collected in step S1 is divided into fixed time window lengths and step sizes; the data in each time window is processed using an improved adaptive median filter algorithm; this algorithm not only calculates the median value of the data in the window, but also dynamically adjusts the mean and standard deviation of the window data, effectively eliminating burst noise and abnormal interference points in the sensor data, and outputting a smooth and stable environmental parameter sequence, specifically:

[0125] Step S21: performing sliding window processing on the original data;

[0126] Step S22: Using an improved median filter algorithm to eliminate impulse noise and achieve adaptive noise suppression, specifically:

[0127] For the original data sequence X={x1,x2,…,x k}, output after filtering:

[0128]

[0129] Where yk is the output value after filtering, median(·) is the median of the data in the window, x k-2 is the k-2th sampling point in the original data sequence, μ is the arithmetic mean of the data in the sliding window, σ is the standard deviation of the data in the sliding window, and e is the base of the natural logarithm.

[0130] Step S3, spatiotemporal feature construction: Dynamic feature calculation is performed on the environmental parameter sequence within each time window after preprocessing in step S2; specifically, the rate of change of magnetic field intensity in three axes (i.e., gradient), the maximum fluctuation amplitude of light intensity within the window time, and the linear trend strength of air pressure value over time are calculated; the calculated magnetic field gradient modulus, light fluctuation rate, and absolute value of air pressure trend coefficient are combined to form a three-dimensional environmental feature vector that characterizes the dynamic changes of the current environment, specifically:

[0131] Step S31: Extract the dynamic characteristics of the environmental parameters in the window, which are:

[0132] Magnetic field gradient tensor

[0133] Where M x ,M y ,M z All are measured values of the three-axis magnetometer, T is the vector transpose sign;

[0134] Light fluctuation rate

[0135] Where L is the sampling sequence of the photosensor in the window, t start ,t end The timestamps of the window start and end respectively;

[0136] Pressure trend coefficient

[0137] Where p i is the i-th air pressure sampling value, is the mean air pressure in the window, t i is the timestamp of the i-th sampling point, is the midpoint of the window time;

[0138] Step S32: The three characteristic parameters in step S31 are normalized and combined into a three-dimensional characteristic vector to form an environmental characteristic matrix containing spatiotemporal correlation characteristics, providing a quantitative basis for the physical environment dimension for dynamic key generation:

[0139] F env =[||G M ||2,R L ,|C P |] T ;

[0140] Where, F env is a three-dimensional eigenvector, ||G M ||2 is the L2 norm of the magnetic field gradient tensor, |C P | is the absolute value of the pressure trend coefficient.

[0141] Step S4, behavioral feature extraction: At the moment the user operates the door lock handle, the pressure sensor array embedded in the handle captures detailed pressure distribution data in real time. Based on this pressure distribution map, the movement trajectory of the pressure center and its angular change rate when the user presses the handle are calculated, and the complexity information entropy of the pressure distribution is calculated. The obtained pressing angle change rate and pressure distribution complexity information entropy are combined to form a two-dimensional behavioral feature vector that characterizes the uniqueness of the current user operation. Specifically,

[0142] Step S41: Using an 8×8 piezoelectric pressure sensor array deployed on the door lock handle, real-time pressure distribution data during user operation is captured;

[0143] Step S42: Calculate the operation characteristics of the handle respectively, specifically:

[0144] Center of pressure offset

[0145] Where x i ,y i is the position coordinate of the i-th unit in the 8×8 pressure sensor array, f i is the pressure value of the i-th unit;

[0146] Angle change rate

[0147] Where Δt is the sampling time interval, arctan(·) is the inverse tangent function, is the change in the pressure center coordinates between adjacent samplings;

[0148] Operation timing complexity

[0149] Where q i is the proportion of each pressure unit;

[0150] Step S43: α and H b Integrate into a two-dimensional behavioral feature vector F act =[α,H b ] T , providing user-specific behavioral characterization parameters for subsequent dynamic key generation.

[0151] Step S5, dual-stream key generation: The environmental feature vector generated in step S3 and the behavioral feature vector generated in step S4 are input into two independent lightweight neural network processing streams respectively; the environmental processing stream performs a linear transformation and nonlinear activation with specific weights on the three-dimensional environmental feature vector, and the behavioral processing stream performs another set of linear transformation and nonlinear activation with specific weights on the two-dimensional behavioral feature vector; the activation output results of the two processing streams are multiplied at the element level, and the fusion result of the multiplication is input into a standard cryptographic hash function for irreversible operation, and finally a fixed-length bit string is intercepted as the dynamic key for the current authentication cycle, specifically:

[0152] Step S51: Design a two-stream cross entropy fusion layer:

[0153]

[0154] Where K is the generated dynamic key, Φ(·) is the SHA-3 hash function, σ(·) is the Sigmoid activation function, and F env is the environmental feature vector, F act is the behavioral feature vector, W e is the environmental flow parameter matrix, W a is the behavior flow parameter matrix, is the Hadamard product;

[0155] Step S52: To optimize the feature expression capability, the environment stream uses a 1×3 convolution kernel to extract cross-dimensional correlation features, and then processes them through batch normalization and GeLU activation function in sequence;

[0156] The behavior stream captures the operation timing pattern through 1×2 convolution kernels, and combines layer normalization and ReLU activation to enhance generalization;

[0157] Step S53: Using the truncation function, extract the lower 128 bits of valid data from the hash output to generate the dynamic key K t =Truncate(K,128bits).

[0158] Step S6, cloud-based dual authentication: The dynamic key generated in step S5 is bitwise XORed with the static master key preset by the user and securely stored locally to obtain the final obfuscated authentication key; the obfuscated authentication key, along with the precise timestamp and device unique identifier, is encrypted using the public key of the cloud-based authentication server through the low-power wide-area IoT communication protocol and transmitted to the cloud-based authentication server; after decrypting within a limited time window, the cloud-based server verifies the validity of the dynamic key using its stored associated information and, based on the verification result, issues an encrypted dynamic control command (such as unlocking, alarming, or maintaining lock) to the door lock. After receiving and executing the command, the door lock updates the local key seed for subsequent dynamic key generation according to the cloud-based command, specifically:

[0159] Step S61: K t XOR confusion with the user's preset master key K0:

[0160]

[0161] Where K final is the final generated 128-bit dynamic authentication key, Bitwise XOR operation, mod 2 128 It is a modular operation;

[0162] Step S62: The obfuscated K final Combined with the current timestamp and device ID, the SM9 identification password is encrypted using the cloud public key and transmitted to the authentication server;

[0163] Step S63: After decryption on the cloud, the legitimacy of the time window is verified and compared with the historical behavior patterns in the environmental feature database; when the authentication is passed, the cloud sends dynamic control instructions to the door lock and synchronously updates the local key seed; when an abnormal environmental feature is detected, the security warning mechanism is triggered and the current authentication request is frozen.

[0164] A specific application of this embodiment is:

[0165] Implementation Background:

[0166] Time: May 28, 2025, 08:00 (Beijing Time)

[0167] Environmental conditions: The background value of the geomagnetic field is 49.8μT; the indoor lighting reference value is 350lx; the standard atmospheric pressure is 1013.25hPa;

[0168] Time accuracy: The synchronization error of all sensor timestamps is ≤0.8ms, ensuring the accuracy of spatiotemporal correlation. Environmental baseline: The magnetic field fluctuation threshold is ±5μT (the geomagnetic field is 49.8μT±10%).

[0169] The threshold for sudden changes in illumination is ±200lx / 100ms; the threshold for changes in air pressure is ±0.1hPa / s;

[0170] Key update mechanism: Dynamically update the master key seed after each authentication to achieve forward security;

[0171] Transmission efficiency: The LoRaWAN data packet size is controlled at 52 bytes (including a 16-byte encryption header).

[0172] Implementation steps:

[0173] Step S1: Synchronous collection of multi-source environmental parameters:

[0174] The three-axis magnetometer collects magnetic field data at a sampling rate of 50 Hz (unit: μT):

[0175] X-axis sequence: 21.5, 21.7, 21.6, 21.9, 22.1, ... (100 data points);

[0176] Y-axis sequence: 45.3, 45.1, 45.2, 45.0, 44.9, ...;

[0177] Z-axis sequence: 32.0, 31.9, 32.2, 32.1, 31.8, ...;

[0178] The light sensor collects light data (unit: lx) at a sampling rate of 50Hz:

[0179] Sequence: 350.2, 351.5, 352.1, 349.8, 350.6, ... (100 data points);

[0180] The MEMS barometer collects pressure data (unit: hPa) at a sampling rate of 50 Hz:

[0181] Sequence: 1013.26, 1013.25, 1013.24, 1013.27, 1013.23, ... (100 data points);

[0182] Time synchronization error: ≤0.8ms (compliant with IEEE1588 protocol);

[0183] Step S2: Dynamic parameter preprocessing:

[0184] Apply an adaptive median filter (window size 5) to the magnetic field X-axis data:

[0185] Take the first 5 points: {21.5, 21.7, 21.6, 21.9, 22.1};

[0186] mean

[0187] Standard Deviation:

[0188] Median value = 21.7 (third after sorting);

[0189] Filter output:

[0190] Repeat for all data points;

[0191] Lighting data filtering (window size 5):

[0192] Take {350.2,351.5,352.1,349.8,350.6};

[0193] Output: 351.2 (calculation process is the same as magnetic field);

[0194] Air pressure data filtering (window size 5):

[0195] Take {1013.26,1013.25,1013.24,1013.27,1013.23};

[0196] Output: 1013.25;

[0197] Step S3: Constructing a spatiotemporal correlation feature matrix:

[0198] Magnetic field gradient calculation (time window 2000ms):

[0199] X-axis gradient:

[0200] Y-axis gradient:

[0201] Z-axis gradient:

[0202] Gradient modulus:

[0203] Light fluctuation calculation:

[0204] Maximum value L max =352.1lx, minimum value L min =349.8lx;

[0205]

[0206] Calculation of air pressure trend coefficient:

[0207] Time series t i :0.0,0.02,…,1.98(s);

[0208] Air pressure series p i :1013.26,1013.25,…,1013.23;

[0209] mean

[0210] molecular

[0211] Denominator

[0212]

[0213] Take the absolute value |C P |=0.054hPa / s;

[0214] Generate environment feature vector: F env =[0.374 1.15 0.054];

[0215] Step S4: User behavior feature extraction:

[0216] Pressure center offset calculation (8×8 piezoelectric matrix):

[0217] Total pressure ∑f i =185.6N;

[0218] Weighted sum of X coordinates:

[0219] Weighted sum of Y coordinates:

[0220] Angle change rate calculation (adjacent 100ms interval):

[0221]

[0222] Operational information entropy calculation:

[0223] Pressure distribution probability

[0224]

[0225] Generate behavioral feature vector: F act =[-5.885.37];

[0226] Step S5: Dual-stream dynamic key generation

[0227] Environmental flow processing (weight matrix W e ):

[0228]

[0229] Linear transformation:

[0230]

[0231] GeLU activation: σ(0.142)≈0.079 (same for others);

[0232] Behavior stream processing (weight matrix W a ):

[0233] W a =[-0.21 0.13 -0.08 0.17 0.15 -0.06 0.22 -0.11];

[0234] Linear transformation:

[0235]

[0236] ReLU activation: max(1.237,0)=1.237 (negative values are reset to zero);

[0237] Dual-stream fusion:

[0238] Hadamard product:

[0239]

[0240] SHA-3 hash: the input byte stream is converted into a 256-bit hash;

[0241] Extract the first 128 bits: A3F18C2D 05B9 E741 6D80 3F2C 9B04 7E2A;

[0242] Step S6: Double authentication protocol execution:

[0243] Key obfuscation operation:

[0244] Default master key K0: 5E82 D3A1 F609 CB74 22D9 0E4C 8A37 B5F1;

[0245] Bitwise XOR:

[0246] K t :A3F18C2D 05B9 E741 6D80 3F2C 9B04 7E2A;

[0247] K0:5E82 D3A1 F609 CB74 22D9 0E4C 8A37 B5F1;

[0248] XOR:FD5F 5F8C F3B0 2C35 4F59 3160 1133 CBDB;

[0249] Data encryption transmission:

[0250] Encapsulate data packets:

[0251] Obfuscation key: FD5F5F8CF3B02C354F5931601133CBDB;

[0252] Environmental characteristics: 0.374, 1.15, 0.054;

[0253] Behavioral characteristics: -5.88, 5.37;

[0254] Timestamp: 2025-05-28T08:00:02.000Z;

[0255] Device ID: DS-LOCK-7A3B;

[0256] SM9 public key encryption is then transmitted via LoRaWAN (channel frequency: 868.125MHz);

[0257] Cloud authentication process: After decryption, extract environmental features and verify that the magnetic field gradient (0.374μT / s) is within the normal range [0.2, 0.5]. Verify that the light fluctuation rate (1.15lx / s) meets the reference range [0.5, 2.0]. Use the master key K0 to restore the dynamic key and recalculate the hash value. If the match is successful, issue the unlock command: ENCRYPTED CMD :8873C2A1B5F09E;

[0258] Local key update:

[0259] Generate a new seed according to the cloud instruction: XOR(K t ,TIMESTAMP);

[0260] The updated master key is: B2D4 8F3C A701 9D56 4E2B 7F89 C0D3 6A1E.

[0261] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0262] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to the specific embodiments described. Obviously, many modifications and variations are possible based on the content of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. An IoT-based smart door lock security management and control system, comprising a local perception layer, an edge computing layer, a cloud service layer, and a user interaction layer, characterized in that: The local perception layer includes a multi-source sensor module, an environment synchronization acquisition unit, a behavior acquisition unit, an environment preprocessing module, and a behavior preprocessing module; The edge computing layer includes a spatiotemporal feature extraction module, a behavior feature modeling module, a dual-stream fusion module, and a dynamic key generation module; The cloud server includes a secure transmission module, an authentication decision module, an environmental feature database and a key management module; The user interaction layer includes a handle pressure array and a user terminal APP; The workflow of the control system is as follows: Step S1, multi-source synchronous acquisition: integrating magnetic, light-sensitive, and air pressure sensors to synchronously acquire environmental data through a time protocol; Step S2, dynamic preprocessing: Segment the data using a sliding window and use an adaptive median filter algorithm to eliminate environmental noise interference; Step S3, spatiotemporal feature construction: extracting magnetic field gradient tensor, illumination fluctuation rate, and air pressure trend coefficient to form a three-dimensional environment feature vector; Step S4, behavioral feature extraction: analyzing the handle pressure distribution, calculating the pressure center offset, angle change rate, and operation entropy value; Step S5, dual-stream key generation: Generate a 128-bit dynamic key by fusing environmental and behavioral features through weighted matrix transformation and lightweight neural network; Step S6, cloud-based dual authentication: After XORing the master key, the SM9 encryption protocol is used to perform interactive verification of time-sensitive data with the cloud.

2. The smart door lock security management and control system based on the Internet of Things according to claim 1 is characterized in that: The output end of the multi-source sensor module is unidirectionally connected to the environment synchronization acquisition unit, the output end of the environment synchronization acquisition unit is unidirectionally connected to the environment pre-processing module, and the output end of the behavior acquisition unit is unidirectionally connected to the behavior pre-processing module; The output end of the environment preprocessing module is unidirectionally connected to the spatiotemporal feature extraction module, the output end of the spatiotemporal feature extraction module is unidirectionally connected to the dual-stream fusion module, the output end of the behavior preprocessing module is unidirectionally connected to the behavior feature modeling module, the output end of the behavior feature modeling module is unidirectionally connected to the dual-stream fusion module, and the output end of the dual-stream fusion module is connected to the dynamic key generation module; The output end of the dynamic key generation module is unidirectionally connected to the secure transmission module, the output end of the secure transmission module is unidirectionally connected to the authentication decision module, and the output end of the authentication decision module is unidirectionally connected to the environmental feature database and the key management module respectively; The handle pressure array output end is unidirectionally connected to the behavior acquisition unit, and the user terminal APP is bidirectionally connected to the authentication decision module.

3. The smart door lock security management and control system based on the Internet of Things according to claim 1 is characterized in that: The multi-source sensor module integrates a magnetometer, a photosensor, and a barometer to collect the magnetic field intensity, light intensity, and air pressure around the door lock in real time; The environmental synchronization acquisition unit is used to achieve timestamp alignment of multi-sensor data through the IEEE 1588 protocol, detect abnormal mutations in magnetic field, light, and air pressure, and filter interference signals that exceed the baseline threshold; The handle pressure array is an 8×8 piezoelectric sensor matrix integrated into the door lock handle. It is used to detect the distribution and dynamic changes of hand pressure during user operation in real time. It generates operation trajectory data through spatial pressure density mapping, providing raw input signals for behavioral feature modeling and capturing the user's unique force application habits. The behavior acquisition unit is used to analyze the distribution characteristics of the 8×8 pressure matrix, track the movement trajectory of the pressure center during handle operation, and generate raw behavior data including spatial offset and temporal complexity; The environmental preprocessing module is used to reduce noise on the original environmental data by using a sliding window and an improved median filter algorithm, and calculate dynamic features such as the magnetic field gradient tensor, illumination fluctuation rate, and air pressure trend coefficient; The behavior preprocessing module is used to extract the angle change rate by calculating the centroid of the pressure distribution, and to construct a feature vector of the user behavior by combining the randomness of the operation sequence with the Shannon entropy quantification; The spatiotemporal feature extraction module is used to extract dynamic features with spatiotemporal correlation from the preprocessed environmental data. By calculating the magnetic field gradient tensor, the light fluctuation rate and the air pressure trend coefficient, it constructs a feature vector reflecting the spatiotemporal variation of environmental parameters, providing a quantitative basis for the physical environment dimension for subsequent dynamic key generation. The behavior feature modeling module is used to convert the original operation data collected by the handle pressure array into a behavior feature vector, calculate the angle change rate through the pressure center offset trajectory, and combine it with Shannon entropy analysis of the operation time sequence to establish a dynamic mathematical model of the user's operation behavior and quantify the uniqueness and randomness characteristics of the behavior pattern; The dual-stream fusion module is used to process the environment and behavior feature streams respectively using a lightweight convolutional network, and realize cross-modal feature coupling through the Hadamard product operation of the cross-entropy fusion layer; The dynamic key generation module is used to perform SHA-3 hash compression and truncation operations on the fusion feature to generate a 128-bit temporary key, and perform XOR confusion with the master key to enhance randomness; The secure transmission module is used to encapsulate data frames based on the LoRaWAN protocol, encrypt dynamic keys using the SM9 algorithm, and attach a digital signature with a timestamp to prevent replay attacks; The authentication decision module is used to verify the validity of the key time window, compare the historical patterns of the cloud environment feature database, decide whether to authorize unlocking and trigger an abnormal warning; The environmental feature database is used to store and manage the temporal and spatial feature baseline data of historical environmental parameters, including the normal fluctuation range and correlation pattern of magnetic field, light, and air pressure in different time periods and geographical locations, providing a dynamic environmental feature comparison benchmark for cloud authentication decisions and identifying abnormal environmental interference or attack behaviors; The key management module is used to dynamically update the master key seed, manage the key distribution and revocation of multiple devices, and record key generation logs for security audit tracing; The user terminal APP is used to provide a human-computer interaction interface for unlocking status visualization, environmental abnormality alarm push, security policy configuration and operation history query.

4. The smart door lock security management and control system based on the Internet of Things according to claim 1 is characterized in that: The step S1, multi-source synchronous acquisition, specifically includes the following steps: Step S11: Integrate a three-axis magnetometer, a micro photosensor, and a MEMS barometer inside the door lock; Step S12: Establish a timestamp synchronization mechanism, using the IEEE 1588 precision time protocol to ensure that the time error of the three sensor data collection is less than 1ms; Step S13: setting environmental parameter baseline thresholds, specifically: magnetic field intensity fluctuation threshold ΔM, illumination mutation threshold ΔL, and air pressure change rate threshold ΔP.

5. The smart door lock security management and control system based on the Internet of Things according to claim 1 is characterized in that: The step S2, dynamic preprocessing specifically includes the following steps: Step S21: performing sliding window processing on the original data; Step S22: Using an improved median filter algorithm to eliminate impulse noise and achieve adaptive noise suppression, specifically: For the original data sequence X={x1,x2,…,x k }, output after filtering: Where y k is the output value after filtering, median(·) is the median of the data in the window, x k-2 is the k-2th sampling point in the original data sequence, μ is the arithmetic mean of the data in the sliding window, σ is the standard deviation of the data in the sliding window, and e is the base of the natural logarithm.

6. The smart door lock security management and control system based on the Internet of Things according to claim 1 is characterized in that: The step S3, constructing the spatiotemporal features, specifically includes the following steps: Step S31: Extract the dynamic characteristics of the environmental parameters in the window, which are specifically: Magnetic field gradient tensor Where M x ,M y ,M z All are measured values of the three-axis magnetometer, T is the vector transpose sign; Light fluctuation rate Where L is the sampling sequence of the photosensor in the window, t start ,t end The timestamps of the window start and end respectively; Pressure trend coefficient Where p i is the i-th air pressure sampling value, is the mean air pressure in the window, t i is the timestamp of the i-th sampling point, is the midpoint of the window time; Step S32: The three characteristic parameters in step S31 are normalized and combined into a three-dimensional characteristic vector to form an environmental characteristic matrix containing spatiotemporal correlation characteristics, providing a quantitative basis for the physical environment dimension for dynamic key generation: F env =[||G M ||2,R L ,|C P |] T ; Where, F env is a three-dimensional eigenvector, ||G M ||2 is the L2 norm of the magnetic field gradient tensor, |C P | is the absolute value of the pressure trend coefficient.

7. The smart door lock security management and control system based on the Internet of Things according to claim 1 is characterized in that: The step S4, extracting behavioral features, specifically includes the following steps: Step S41: Using an 8×8 piezoelectric pressure sensor array deployed on the door lock handle, real-time pressure distribution data during user operation is captured; Step S42: Calculate the operation characteristics of the handle respectively, specifically: Center of pressure offset Where x i ,y i is the position coordinate of the i-th unit in the 8×8 pressure sensor array, f i is the pressure value of the i-th unit; Angle change rate Where Δt is the sampling time interval, arctan(·) is the inverse tangent function, is the change in the pressure center coordinates between adjacent samplings; Operation timing complexity Where q i is the proportion of each pressure unit; Step S43: α and H b Integrate into a two-dimensional behavioral feature vector F act =[α,H b ] T , providing user-specific behavioral characterization parameters for subsequent dynamic key generation.

8. The smart door lock security management and control system based on the Internet of Things according to claim 1 is characterized in that: The step S5, generating the dual-stream key specifically includes the following steps: Step S51: Design a two-stream cross entropy fusion layer: Where K is the generated dynamic key, Φ(·) is the SHA-3 hash function, σ(·) is the Sigmoid activation function, and F env is the environmental feature vector, F act is the behavioral feature vector, W e is the environmental flow parameter matrix, W a is the behavior flow parameter matrix, is the Hadamard product; Step S52: Optimizing feature expression capability, the environment flow uses a 1×3 convolution kernel to extract cross-dimensional correlation features, and then processes them through batch normalization and GeLU activation function in sequence; The behavior stream captures the operation timing pattern through 1×2 convolution kernels, and combines layer normalization and ReLU activation to enhance generalization; Step S53: Using the truncation function, extract the lower 128 bits of valid data from the hash output to generate the dynamic key K t =Truncate(K,128bits).

9. The smart door lock security management and control system based on the Internet of Things according to claim 1 is characterized in that: Said step S6, cloud-based dual authentication specifically includes the following steps: Step S61: K t XOR confusion with the user's preset master key K0: Where K final is the final generated 128-bit dynamic authentication key, Bitwise XOR operation, mod 2 128 It is a modular operation; Step S62: The obfuscated K final Combined with the current timestamp and device ID, the SM9 identification password is encrypted using the cloud public key and transmitted to the authentication server; Step S63: After decryption on the cloud, the legitimacy of the time window is verified and compared with the historical behavior patterns in the environmental feature database; when the authentication is passed, the cloud sends dynamic control instructions to the door lock and synchronously updates the local key seed; when an abnormal environmental feature is detected, the security warning mechanism is triggered and the current authentication request is frozen.

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