Intelligent lock multi-mode authority management and record tracing method and system

Abstract

The invention provides a multi-mode authority management and record tracing method and system for an intelligent lock. The method comprises the following steps: step 1, obtaining the intelligent lock, performing initialization setting on the intelligent lock, connecting the intelligent lock to a home local area network, and establishing secure communication connection with a cloud server; according to the invention, multiple identity recognition modes are supported, multiple permission types are provided, and a permission type combination strategy can be formulated, so that the flexibility of permission management is greatly improved, and diversified use requirements of modern families are met; according to the method, unlocking information of each time is automatically recorded, and a user behavior model is constructed in combination with a big data analysis technology, so that potential safety risks and abnormal behavior modes can be quickly and accurately identified, and the efficiency and accuracy of troubleshooting are improved; the intelligent lock can be quickly and automatically switched to the emergency mode when facing a complex environment or a special condition, so that the malicious attack resisting capability of the system is effectively improved, and the safety and the stability of the intelligent lock in the complex environment are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of smart locks, and in particular to a smart lock multimodal authority management and record tracing method and system thereof. Background Art

[0002] At a time when technology is rapidly iterating, smart home technology is like a rising star, sweeping the entire home furnishing field with its innovation and convenience. From smart air conditioners that can automatically adjust the temperature according to the environment, to smart home appliances that can be remotely controlled, to smart sweeping robots that can automatically clean, various smart home products are constantly being innovated, bringing unprecedented comfort and convenience to people's lives. In the smart home ecosystem, smart locks, as a key line of defense and important entrance for home security, are gradually becoming the standard of modern families with their unique advantages. However, although smart locks have been widely used in the market, there are still many problems that need to be solved in the current smart lock technology.

[0003] 1. The traditional smart lock permission management method is relatively simple, usually only supporting a few unlocking methods such as passwords and fingerprints, and the permission allocation and management are not flexible enough to meet the diverse usage needs of modern families; Second, in terms of authority recording and tracing, the existing technology has problems such as inadequate recording and difficulty in tracing, and it is impossible to accurately record the specific circumstances of each unlocking. When security problems occur, it is difficult to quickly and accurately identify the cause; 3. The security and stability of existing smart locks need to be further improved when facing complex environments or special situations, such as network failures, malicious attacks, etc. To this end, a smart lock multimodal permission management and record tracing method and system are proposed. Summary of the invention

[0004] In view of this, the embodiments of the present invention hope to provide a smart lock multimodal permission management and record tracing method and system thereof to solve or alleviate the technical problems existing in the prior art and at least provide a beneficial option.

[0005] In order to solve the above technical problems, a technical solution adopted by the present application is: a smart lock multimodal permission management and record tracing method, comprising the following steps: Step 1: Get a smart lock, initialize the smart lock, connect it to the home LAN, and establish a secure communication connection with the cloud server; Step 2: Enter various identity information through the management APP of the smart lock, encrypt it, and then upload it to the local storage module of the smart lock, the cloud server, and the edge computing node; Step 3: Assign permission types to different users based on the management APP and formulate a permission type combination strategy; Step 4: When the user unlocks the lock, the smart lock obtains the corresponding identification information according to the identification method selected by the user, and performs a preliminary match in the local storage module. If the local match fails, a request is sent to the cloud server and edge computing node through a secure channel for remote matching verification; Step 5: The smart lock automatically records the unlocking information of each unlocking operation and stores it in the local storage module of the smart lock, and synchronizes it to the cloud server and edge computing node in real time; Step 6: Obtain unlocking records from cloud servers or edge computing nodes through the management app, and use big data analysis technology to conduct in-depth mining and analysis to identify potential security risks and abnormal behavior patterns; Step 7. When the smart lock encounters a network failure, system failure or is attacked by a malicious attack, it automatically switches to emergency mode and performs a limited number of unlocking operations in emergency mode based on the locally stored permission information and backup identity recognition methods.

[0006] As a further preferred embodiment of the present technical solution, in step six, the big data analysis technology uses a machine learning algorithm to train historical unlocking records and construct a user behavior model; The method for constructing a user behavior model comprises the following steps: Step 601: Obtain historical unlocking record data from the cloud server and the edge computing node, and pre-process the historical unlocking record data; Step 602: extract valuable feature data from the pre-processed historical unlocking record data, and divide the feature data into a training set, a verification set, and a test set; Step 603: Select a decision tree, support vector machine or deep learning neural network in the machine learning algorithm as the basic architecture of the model; Step 604: Use the training set and the validation set as input, and use normal behavior and abnormal behavior as output labels to train the model; Step 605: Use the test data set to evaluate the trained model, calculate the accuracy, recall rate, and F1 value, and optimize and adjust the model based on the evaluation results.

[0007] As a further preferred embodiment of the present technical solution, in step 4, a multi-factor authentication mechanism is adopted during the remote matching verification, and the specific steps are as follows: Step 401: Establish user behavior pattern characteristics by analyzing the user's previous unlocking time, unlocking frequency, and common identity recognition methods, and obtain the user's historical behavior pattern; Step 402: Acquire the user's current geographic location information by cooperating with the positioning function of the user's device; Step 403: Obtain device fingerprint information of the user operating the device by collecting the hardware information and software information of the device; Step 404: Combine historical behavior patterns, geographic location information, and device fingerprint information with identity information to make a comprehensive judgment based on preset rules and algorithms; Step 405: If all verification conditions are passed, the smart lock performs the unlocking operation; if the verification fails, the unlocking is rejected and the detailed information of the abnormal unlocking attempt is recorded.

[0008] Provided as a further preferred embodiment of the present technical solution, in step one, the smart lock supports multiple identity recognition modes, including fingerprint recognition, facial recognition, iris recognition, voiceprint recognition, password input, Bluetooth unlocking, NFC card unlocking and card swiping unlocking; the secure communication connection uses encryption protocols SSL or TLS.

[0009] As a further preferred embodiment of the present technical solution, in step five, the unlocking information includes the unlocking time, the identification method of the unlocking user, the identity information, whether the unlocking is successful, the environmental parameters around the smart lock when unlocking, and the user's operation behavior trajectory.

[0010] As a further preferred embodiment of the present technical solution, in step three, the permission types include permanent permissions, temporary permissions, periodic permissions and specific scenario permissions; the permission type combination strategy includes a combination of multiple identity identification methods, a combination of different permission types and usage restrictions of different identity identification methods under different permission types.

[0011] As a further preferred embodiment of the present technical solution, in step 2, the identity recognition information includes fingerprint recognition information, facial recognition information, voiceprint recognition information and iris recognition information; the encryption processing adopts an advanced encryption standard algorithm to encrypt the identity recognition information, and uses different keys to encrypt information of different users.

[0012] To solve the above technical problems, another technical solution adopted by this application is: a smart lock multimodal permission management and record tracing system, including: a smart lock device module, a management APP module, a cloud server module, an edge computing node module, a multi-factor authentication module, a big data analysis module and an emergency processing module; The smart lock device module is used to obtain the user's identity information through the built-in multiple identity recognition modes, and perform a preliminary match on the obtained identity information in the local storage module. If the match fails, a remote matching verification request is sent to the cloud server module and the edge computing node module through a secure channel for remote matching verification; The management APP module is used to provide a user operation interface, support users to enter a variety of identity information through the module, and encrypt the entered information and send it to the smart lock device module, cloud server module and edge computing node module; The cloud server module is used to receive and store the identity information, authority information, and unlocking records uploaded by the smart lock device module; provide data access services for the management APP module to obtain unlocking records within a specified time period; The edge computing node module is used to receive a remote matching verification request sent by the smart lock device module and assist in matching verification of identity information; The multi-factor authentication module is used to obtain historical behavior patterns, geographic location information and device fingerprint information, and make comprehensive judgments based on preset rules and algorithms in combination with identity recognition information; The big data analysis module is used to obtain historical unlocking record data from the cloud server module and the edge computing node module, and use machine learning algorithms to train historical unlocking records, build user behavior models, and identify potential security risks and abnormal behavior patterns; The emergency processing module is used to automatically trigger the emergency mode when the smart lock device module encounters a network failure, a system failure or is attacked by a malicious attack.

[0013] As a further preferred embodiment of the present technical solution, the local storage module in the smart lock device module adopts encrypted storage technology to encrypt the stored identity information, authority information and unlocking records, and the local storage module has data backup and recovery functions; the management APP module has a user behavior analysis function, which is used to analyze the user's operating habits and preferences based on the user's operation records of the smart lock.

[0014] As a further preferred embodiment of the present technical solution, the cloud server module adopts a distributed storage architecture to store data in a dispersed manner on multiple storage nodes; in the emergency mode, the emergency processing module sends the operating status and emergency situation of the smart lock to the administrator in real time via SMS or push notification.

[0015] The embodiment of the present invention has the following advantages due to the adoption of the above technical solution: 1. The present invention supports multiple identity recognition modes and provides multiple permission types. It can also formulate permission type combination strategies. Administrators can accurately allocate permissions to different users based on actual conditions, which greatly improves the flexibility of permission management and meets the diverse usage needs of modern families. 2. The present invention automatically records each unlocking information and synchronizes it to the cloud server and edge computing node in real time. It combines big data analysis technology to build a user behavior model, which can quickly and accurately identify potential security risks and abnormal behavior patterns, providing a powerful and accurate basis for identifying the causes, and significantly improving the efficiency and accuracy of problem troubleshooting; 3. When faced with complex environments or special circumstances, the smart lock of the present invention will automatically switch to emergency mode quickly to ensure the integrity and security of the system and the consistency of data, effectively improving the system's ability to resist malicious attacks and further enhancing the security and stability of the smart lock in complex environments.

[0016] The above summary is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments and features described above, further aspects, embodiments and features of the present invention will be readily apparent by reference to the accompanying drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 A flowchart of a multi-modal permission management and record tracing method for a smart lock according to the present invention; Figure 2 A schematic diagram of a process for constructing a user behavior model according to the present invention; Figure 3 A schematic diagram of a flow chart of a method for adopting a multi-factor authentication mechanism in the present invention; Figure 4 This is a functional module diagram of a smart lock multimodal authority management and record tracing system of the present invention. DETAILED DESCRIPTION

[0019] The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0020] It should be clear that the following embodiments of the present disclosure are described by specific specific examples, and those skilled in the art can easily understand other advantages and effects of the present disclosure from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all of the embodiments. The present disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that the following embodiments and features in the embodiments can be combined with each other in the absence of conflict. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present disclosure.

[0021] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein may be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on the present disclosure, it should be understood by those skilled in the art that an aspect described herein may be implemented independently of any other aspect, and two or more of these aspects may be combined in various ways. For example, any number of aspects described herein may be used to implement the device and / or practice the method. In addition, other structures and / or functionalities other than one or more of the aspects described herein may be used to implement this device and / or practice this method.

[0022] It should also be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present disclosure. The drawings only show components related to the present disclosure rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0023] Additionally, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects described may be practiced without these specific details.

[0024] Figure 1 1 is a flowchart of a method for multi-modal permission management and record tracing of a smart lock according to an embodiment of the present invention. It should be noted that if there are substantially the same results, the method of this application is not based on Figure 1 The process sequence shown is limited. Figure 1-Figure 3 As shown: A smart lock multi-modal permission management and record tracing method, comprising the following steps: Step 1: Get a smart lock, initialize the smart lock, connect it to the home LAN, and establish a secure communication connection with the cloud server; Specifically, first, users need to obtain a smart lock device, which is the hardware foundation for realizing the entire multimodal permission management and record tracing functions, install the smart lock on the required door, and ensure that its physical structure is stable and can properly perform the basic functions of the lock, such as locking and opening the door; Then, turn on the power of the smart lock to put it into working state. The user needs to perform a series of initial settings for the smart lock, including but not limited to selecting the display language, setting the initial time, etc. These basic settings will help the subsequent operations go smoothly and provide the system with the necessary time reference; Finally, the user needs to enter the name (SSID) and password of the home wireless network on the operation interface of the smart lock so that the smart lock can access the home LAN. The importance of this step lies in integrating the smart lock into the home network environment, laying the foundation for subsequent communication with other smart devices and cloud servers; in order to ensure the security of communication between the smart lock and the cloud server, an encryption protocol (such as SSL or TLS) is used to establish a connection. During the connection process, the smart lock and the cloud server will exchange encryption keys to ensure that the data transmitted between them will not be stolen or tampered with. In this way, whether it is subsequent identity identification information, permission information or unlocking records and other data, they are all in a secure encrypted state during transmission, enhancing the information security of the entire system.

[0025] Step 2: Enter various identity information through the management APP of the smart lock, encrypt it, and then upload it to the local storage module of the smart lock, the cloud server, and the edge computing node; Specifically, users use the management APP that comes with the smart lock to input various identity recognition information, such as fingerprints, facial images, iris images, voiceprint audio, etc. For fingerprint information, users may need to enter fingerprints of different fingers or the same finger at different angles multiple times to ensure the accuracy of recognition; for facial information, users may need to enter facial images under different lighting conditions to allow the system to learn and store the user's facial features; when entering voiceprint information, users need to read some specific sentences, and the system will record the user's voice characteristics; for iris information, it needs to be collected at a specific distance and angle to ensure that a clear and accurate iris image is obtained; in order to protect the user's privacy and information security, the entered identity recognition information is encrypted using the Advanced Encryption Standard (AES) algorithm, and different users' information is encrypted using different keys, so that even if the information is stolen, it is difficult to decrypt, ensuring the security and uniqueness of the user's information.

[0026] Step 3: Assign permission types to different users based on the management APP and formulate a permission type combination strategy; Specifically, administrators or users can use the management app to assign permissions to different users. There are many types of permissions, including: Permanent permissions: grant users long-term, unlimited unlocking permissions, suitable for key family members, who can use the smart lock at any time without being restricted by time or other conditions; Temporary permissions: Provided for temporary visitors or short-term users, the administrator can set a valid time period, such as from a certain time to a certain time on a certain day, and the user can only use the smart lock within this time period; Periodic permissions: can be set according to the user's daily usage patterns, such as specific time periods on weekdays or weekends. Users can only unlock the door during these periods, which is suitable for regular usage scenarios; Specific scene permissions: Determine whether to allow unlocking based on specific environmental conditions or events. For example, users can only use smart locks when the indoor temperature is within a certain range, the light intensity reaches a certain level, or other smart devices are in a specific state. In addition, administrators or users can combine different permission types or use multiple identity recognition methods in combination to generate different permission type combination strategies to increase the flexibility and security of permission management; for example, it can be set to unlock the door only when fingerprint recognition and password input are met at the same time, or users with temporary permissions can only use specific identity recognition methods (such as NFC card unlocking).

[0027] Step 4: When the user unlocks the lock, the smart lock obtains the corresponding identification information according to the identification method selected by the user, and performs a preliminary match in the local storage module. If the local match fails, a request is sent to the cloud server and edge computing node through a secure channel for remote matching verification; Specifically, when a user tries to unlock the door, he or she first needs to select an identification method, such as using a fingerprint, entering a password, or using an NFC card. The smart lock will obtain the identification information provided by the user through the corresponding sensor or input device, such as a fingerprint sensor, a password keyboard, or an NFC card reader; The smart lock will then compare the identification information entered by the user with the information stored in the local storage module. This process is to quickly verify the user's identity. If the match is successful, the unlocking operation may be performed immediately; if there is no matching information in the local storage module or the match is unsuccessful, a request will be sent to the cloud server and edge computing node through a secure channel for remote matching verification.

[0028] Step 5: The smart lock automatically records the unlocking information of each unlocking operation and stores it in the local storage module of the smart lock, and synchronizes it to the cloud server and edge computing node in real time; Specifically, regardless of whether the unlocking operation is successful, the smart lock will record detailed unlocking information, including: unlocking time, unlocking user's identity identification method, identity information, whether the unlocking is successful, environmental parameters around the smart lock when unlocking, user's operation behavior trajectory, etc.; and store the above information in the local storage module as a local data backup. At the same time, in order to facilitate remote management and data sharing, it is synchronized to the cloud server and edge computing node in real time to ensure data redundancy and consistency.

[0029] Step 6: Obtain unlocking records from cloud servers or edge computing nodes through the management app, and use big data analysis technology to conduct in-depth mining and analysis to identify potential security risks and abnormal behavior patterns; Specifically, administrators or users can query unlocking records from cloud servers or edge computing nodes through the management APP. These records store all previous unlocking information and provide a data basis for subsequent analysis and management. These unlocking records are processed by using big data analysis technology, including: training historical unlocking records and building user behavior models. Through machine learning algorithms, the system can learn the user's normal and abnormal behavior patterns.

[0030] Step 7: When the smart lock encounters a network failure, system failure, or is attacked by a malicious attack, it automatically switches to an emergency mode, and performs a limited number of unlocking operations in the emergency mode based on the locally stored permission information and backup identity recognition methods; Specifically, when the smart lock encounters a network failure (such as a home network interruption), a system failure (such as a problem with the smart lock's internal software or hardware), or is attacked by malicious means (such as a hacker attack attempting to crack the lock), the system will automatically switch to emergency mode; In emergency mode, the smart lock will perform a limited number of unlocking operations based on the locally stored permission information and backup identity recognition methods. The backup identity recognition methods may include emergency passwords, physical keys, etc. This information is stored and encrypted in the local storage module to ensure that users can still enter the house under special circumstances. At the same time, in order to prevent abuse, the number of uses will be limited to ensure the security of the system; The existence of emergency mode ensures the basic availability of smart locks in abnormal situations, preventing users from being unable to enter their homes due to network or system problems. It also takes security considerations into account to prevent unauthorized unlocking operations. At the same time, the smart lock continues to try to restore connections with cloud servers and edge computing nodes.

[0031] In one embodiment, specifically: in step six, the big data analysis technology uses a machine learning algorithm to train historical unlocking records and build a user behavior model; The method of building a user behavior model includes the following steps: Step 601: Obtain historical unlocking record data from the cloud server and the edge computing node, and pre-process the historical unlocking record data; Specifically, first, the system needs to obtain a large amount of historical unlocking record data from the cloud server and edge computing nodes. This data contains all previous unlocking operation information, covers the unlocking behaviors of different users at different times and under different conditions, and is the basis for building a user behavior model. For example, it may include information such as user A successfully unlocking the door using fingerprint recognition at 8 a.m. on Monday, and user B failing to unlock the door using password input at 9 p.m. on Wednesday. Then, data cleaning is used to remove data that is obviously wrong or incomplete. For example, incomplete records may be caused by network failure or other reasons. For example, if there is a lock unlocking record that lacks key identification information, it will be removed from the data set. For missing data in some records, fill it in according to the characteristics of the data. For example, if ambient temperature information is missing in some records, it may be filled in according to the average temperature in other normal records, or more complex methods may be used, such as using time series prediction algorithms to estimate the missing temperature values. Different types of data are converted into a unified format. For example, time data is converted into timestamps, and identification methods (such as fingerprints, passwords, Bluetooth, etc.) are encoded for subsequent algorithm processing. For environmental parameters, they are normalized to a standard range, such as converting temperature from degrees Celsius to a range of 0 to 1 to eliminate the impact of different data magnitudes.

[0032] Step 602: extract valuable feature data from the pre-processed historical unlocking record data, and divide the feature data into a training set, a verification set, and a test set; Specifically, first, key features are extracted from the preprocessed data. These features will be used as input to the model to distinguish normal behavior from abnormal behavior; for example: Time feature: Extract the specific time of unlocking, day of the week, whether it is a holiday, whether it is working hours, etc., because the user's unlocking behavior may have a temporal pattern, such as users usually unlock the lock in the morning when going to work on weekdays, or the unlocking frequency is low on weekend nights; Identity recognition characteristics: record the identity recognition methods used (such as fingerprint, password, NFC card, etc.) and the frequency of use of different identity recognition methods. Some abnormal behaviors may be manifested by frequent changes in identity recognition methods; Behavioral characteristics: Calculate the number of consecutive unlocking failures, the number of unlocking attempts in a short period of time, the unlocking frequency in different time periods, etc. These characteristics can reflect whether the user is performing abnormal operations. For example, multiple attempts to unlock the door in a short period of time may be abnormal behavior; Environmental characteristics: Extract environmental parameters when unlocking, such as temperature, humidity, light intensity, etc. Environmental abnormalities may affect the user's normal unlocking behavior and may also be a signal of abnormal operation; Then, the extracted feature data is divided into training set, validation set and test set, usually according to a certain ratio, such as 70% as training set, 15% as validation set, and 15% as test set. The training set is used to train the model, the validation set is used to adjust the parameters of the model during the training process to prevent overfitting, and the test set is used to finally evaluate the performance of the model; for example, 700 data are used as training set, 150 as validation set, and 150 as test set.

[0033] Step 603: Select a decision tree, support vector machine or deep learning neural network in the machine learning algorithm as the basic architecture of the model; Among them, the decision tree algorithm is a classification algorithm based on a tree structure, which classifies data through a series of decision rules. For the user behavior model, it can gradually determine whether it is normal or abnormal behavior based on different features, such as time, identity recognition method, environmental conditions, etc. For example, first determine whether it is a weekday, and if so, then determine whether the identity recognition method used is a common method used by users, and so on, to build a tree-like judgment logic. The advantage of the decision tree is that it is easy to understand and explain, and is suitable for scenarios with clear logical judgments on feature relationships. The support vector machine algorithm separates data of different categories by finding a hyperplane. For the user behavior model, normal behavior and abnormal behavior data can be regarded as different categories. The data is mapped to a high-dimensional space through a kernel function to find an optimal hyperplane for classification. For example, for complex data distribution, it can handle nonlinear classification problems. When the relationship between user behavior and factors such as environment and time is not a simple linear relationship, the support vector machine can find a better decision boundary. The deep learning neural network algorithm contains multiple hidden layers, which can automatically learn complex patterns in the data and has a strong learning ability for complex user behavior patterns. For example, it can automatically learn the complex behavioral characteristics of users in different environments, at different times, and in different identity recognition methods, and obtain the final classification result through the calculation of multiple layers of neurons and the processing of activation functions. For large amounts of data and complex behavior patterns, deep learning neural networks may achieve better results, but they require more computing resources and time for training.

[0034] Step 604: Use the training set and the validation set as input, and use normal behavior and abnormal behavior as output labels to train the model; The specific training process is as follows: First, the feature data in the training set is used as input, and each sample is labeled as normal behavior or abnormal behavior as the output label; for example, a record in which a user successfully unlocks the door with a fingerprint on a weekday morning is labeled as normal behavior, and a record in which a user fails to unlock the door with a password multiple times at 3 a.m. is labeled as abnormal behavior; Then, the model is trained using the selected machine learning algorithm. The model adjusts its own parameters based on the input feature data and corresponding labels to learn the characteristic patterns of normal and abnormal behaviors. For example, a decision tree adjusts the decision rules of its branch nodes, a support vector machine adjusts the position of the hyperplane, and a deep learning neural network adjusts the weights and biases of each layer to minimize prediction errors. At the same time, during the training process, the validation set is used for verification to ensure that the model does not overfit. Overfitting means that the model over-learns the details of the training data, resulting in reduced performance on new data. The validation set can be used to detect and adjust in a timely manner.

[0035] Step 605: Use the test data set to evaluate the trained model, calculate the accuracy, recall rate, and F1 value, and optimize and adjust the model according to the evaluation results; For accuracy: the data in the test set is input into the trained model, and the proportion of samples correctly predicted by the model to the total number of samples is calculated; for example, if there are 100 samples in the test set and the model correctly predicts 80, the accuracy is 80%; For recall rate: it is obtained by calculating the proportion of samples that are actually abnormal behaviors that are correctly predicted as abnormal behaviors; for example, there are actually 20 abnormal behavior samples, and the model correctly predicts 15 of them, the recall rate is 75%; For the F1 value: it takes into account both the accuracy and recall rate, and is the harmonic mean of the two, which is used to balance the precision and recall rate of the model; for example, if the accuracy rate is 80% and the recall rate is 75%, the F1 value is calculated by the formula to comprehensively evaluate the model performance; suppose there is a test data set with 100 samples, and the model prediction results are as follows: There are 30 actual abnormal behavior samples (positive class), 20 of which are correctly predicted as abnormal behaviors by the model, 10 of which are incorrectly predicted as abnormal behaviors, and 10 of which are incorrectly predicted as normal behaviors; First calculate the precision and recall: ; ; Then calculate the F1 value: ; Based on the evaluation results, if the model performance does not meet the requirements, the model will be optimized and adjusted; For example, for decision trees, you can adjust the depth of the tree and pruning operations to prevent overfitting; for support vector machines, you can adjust the parameters of the kernel function; for deep learning neural networks, you can adjust the number of network layers, the number of neurons, the learning rate, etc. At the same time, you can also consider re-extracting features, or adding more data for training, and continuously optimize the model so that it can accurately identify abnormal user behavior in actual applications and improve the security and reliability of the system; Through the above process of building a user behavior model, the system can use historical data and machine learning algorithms to accurately identify normal and abnormal behaviors of users, provide more intelligent and effective support for the security management of smart locks, discover potential security risks in advance, and ensure the safety of families and users.

[0036] In one embodiment, specifically: in step 4, during the remote matching verification, a multi-factor authentication mechanism is adopted, and the specific steps are as follows: Step 401: Establish user behavior pattern characteristics by analyzing the user's previous unlocking time, unlocking frequency, and common identity recognition methods, and obtain the user's historical behavior pattern; Specifically, the system will collect the user's previous unlocking operation information from the stored historical unlocking records, including the specific time of each unlocking, unlocking frequency, and the identity recognition method used; for example, the system will record the time when the user unlocks the door every day in the past month, and it may be found that the user usually uses fingerprint recognition to unlock the door between 7:30 and 8:00 in the morning, which is a typical user behavior pattern; Among them, establishing user behavior pattern characteristics includes: unlocking time mode, unlocking frequency and common identity recognition methods; Unlocking time pattern: Analyze the distribution of users' unlocking time, such as whether users have a fixed unlocking time range, whether it is concentrated on weekdays or weekends, whether they unlock in the morning, at noon or at night, etc. Unlock frequency: count the number of times a user unlocks the door within a certain period of time (such as a day, a week, or a month) to determine whether the user's usage frequency is stable, and whether there are abnormally frequent or rare unlocking situations; Commonly used identification methods: Determine which identification method the user prefers, such as whether they often use fingerprints or passwords, or whether they switch to different identification methods in different scenarios.

[0037] Step 402: Acquire the user's current geographic location information by cooperating with the positioning function of the user's device; Specifically, the smart lock system will cooperate with the positioning function of the user's operating device (such as the user's mobile phone) to obtain the user's current geographical location information; this can be achieved through a variety of positioning technologies, such as GPS positioning, Wi-Fi positioning or base station positioning; for example, if the user's mobile phone has the GPS function turned on, the smart lock system can obtain the user's latitude and longitude information through communication with the mobile phone; This information can be used to determine whether the user is unlocking the door within a reasonable geographical location. For example, a user usually only uses a smart lock near home or work. If the current location is far from the user's usual location, it may be an abnormal behavior.

[0038] Step 403: Obtain device fingerprint information of the user operating the device by collecting the hardware information and software information of the device; Specifically, first, collect the hardware identifiers of the user's operating device, such as the device's MAC address, IMEI code (for mobile phones), CPU serial number, hard disk serial number, etc. This hardware information is usually unique for each device and can be used as an identifier of the device; Then, collect software information such as the operating system version, application version, and software installation list of the user's device. Different software versions or applications may be associated with different security risks and can also be used as one of the characteristics of the device; Finally, by performing specific hash operations or feature extraction algorithms on the collected hardware and software information, device fingerprint information is generated. The device fingerprint information can uniquely identify the user's operating device and prevent the user from using unauthorized devices to unlock the door.

[0039] Step 404: Combine historical behavior patterns, geographic location information, and device fingerprint information with identity information to make a comprehensive judgment based on preset rules and algorithms; Specifically, first, the historical behavior patterns, geographic location information, device fingerprint information, and identity information (such as fingerprint, password, etc.) provided by the current user obtained in the previous steps are aggregated together; Among them, the preset rules and algorithms include the following aspects: Time rule: Determine whether the current unlocking time is consistent with the user's historical behavior pattern. For example, if the user usually does not unlock the door after 12 o'clock in the evening, and the current operation is at 2 o'clock in the morning, it may trigger an exception; Location rule: Determine whether the current location is within the user's frequently visited locations. For example, the user's frequently visited location is the area where their home is located. If the current location is in a distant city, there may be risks. Device fingerprint rule: Verify whether the device fingerprint of the currently operated device matches the fingerprint of the device previously used by the user. If they do not match, it may be an unfamiliar device operation; Identification rules: Verify whether the currently provided identification information is consistent with the stored information, and combine other factors, such as whether the fingerprint is within the user's commonly used fingerprint range, whether the password is correct and has not expired, etc. When making a comprehensive judgment, different weights will be assigned to different factors. For example, historical behavior patterns account for 30% of the weight, geographic location information accounts for 20%, device fingerprint information accounts for 30%, and identity recognition information accounts for 20%. Whether the verification is passed is determined through weighted calculation and threshold setting.

[0040] Step 405: If all verification conditions are passed, the smart lock performs the unlocking operation; if the verification fails, the unlocking is rejected and the detailed information of the abnormal unlocking attempt is recorded; Specifically, if the comprehensive judgment result meets the preset security standards, that is, all verification conditions are passed, the smart lock will perform the unlocking operation to allow the user to enter; if the verification fails, the smart lock will refuse to unlock and record detailed information, including: the time of the unlocking attempt, accurate to the second, for subsequent tracing; the identity recognition method attempted, whether it is fingerprint, password or other methods, to help determine the possible problem; the specific cause of the abnormality, whether it is due to location mismatch, device mismatch or identity recognition error, etc., to provide clues for subsequent security analysis; and store these abnormal information in the local storage module, cloud server or edge computing node for subsequent monitoring and processing by the administrator or system, such as notifying the administrator of abnormal unlocking attempts, or conducting further risk assessment; Through this multi-factor authentication mechanism, the smart lock system not only relies on a single identity recognition information, but also comprehensively considers information from multiple dimensions to form a comprehensive security protection system, which greatly improves the security of users using smart locks and reduces the risk of being cracked or illegally operated.

[0041] In one embodiment, specifically: in step one, the smart lock supports multiple identity recognition modes, including fingerprint recognition, facial recognition, iris recognition, voiceprint recognition, password input, Bluetooth unlocking, NFC card unlocking and card swiping unlocking; wherein, fingerprint recognition recognizes the unique texture features of the user's fingerprint, facial recognition uses a camera to capture the user's facial features, iris recognition recognizes the unique pattern of the user's iris, password input requires the user to enter a pre-set password, Bluetooth unlocking allows the user to unlock the lock by pairing the mobile phone Bluetooth with the smart lock, NFC card unlocking uses near-field communication technology, and card swiping unlocking uses a magnetic card or radio frequency card to unlock the lock. These recognition modes each have their own characteristics and advantages, meet the usage habits and security needs of different users, and provide a variety of identity authentication methods for subsequent multi-modal permission management; Secure communication connections use encryption protocols SSL or TLS; SSL is a protocol developed by Netscape to ensure network communication security, while TLS is the successor version of SSL, developed on the basis of SSL3.0. The two are similar in function and principle, and are usually collectively referred to as SSL / TLS. They work between the network transport layer and the application layer to provide encryption, identity authentication and data integrity protection for data transmission; Encryption: When the smart lock establishes a connection with the cloud server, the SSL / TLS protocol uses symmetric encryption algorithms (such as AES) and asymmetric encryption algorithms (such as RSA) to encrypt the transmitted data. First, a symmetric encryption key is securely exchanged through an asymmetric encryption algorithm (such as using the server's public key), and then the symmetric key is used to encrypt and decrypt the actual transmitted data. This can improve the efficiency of data encryption and decryption while ensuring the secure transmission of the key; Identity authentication: SSL / TLS protocol uses digital certificates to verify the identities of both parties. When the smart lock communicates with the cloud server, the server will send its own digital certificate to the smart lock. The certificate is signed by a trusted certificate authority (CA). The smart lock will verify the validity of the certificate, including checking whether the certificate is expired and whether the certificate authority is trusted, thereby confirming the identity of the server. Similarly, in some cases, the smart lock may also need to provide its own digital certificate to the server to prove its identity; Data integrity protection: SSL / TLS protocol uses message authentication code (MAC) or hash function (such as SHA-256) to ensure that data has not been tampered with during transmission. The sender will calculate the hash value of the data and send it together with the data to the receiver. After receiving the data, the receiver recalculates the hash value of the data and compares it with the received hash value. If the two hash values ​​are consistent, it means that the data has not been tampered with during transmission, ensuring the integrity of the data. Function in smart lock system: Protect user data security: When the smart lock communicates with the cloud server, it will transmit sensitive data such as the user's identity information (such as fingerprints, facial recognition information, etc.), permission information, and unlocking records. Using the SSL / TLS protocol to encrypt and transmit this data can prevent the data from being stolen by hackers during transmission, protecting the user's privacy and security; Prevent man-in-the-middle attacks: Through identity authentication and data integrity protection, the SSL / TLS protocol can effectively prevent man-in-the-middle attacks, which are attacks in which hackers insert themselves between communicating parties to intercept, tamper with, and forge communication data. The SSL / TLS protocol verifies the identities of both communicating parties and ensures the integrity of data, making it impossible for hackers to carry out man-in-the-middle attacks without being discovered. Ensure the reliability of communication: The SSL / TLS protocol can also ensure the reliability of communication. It can detect errors and anomalies in the data transmission process and take corresponding measures to deal with them, such as retransmitting data, etc., to ensure that the communication between the smart lock and the cloud server can be stable and reliable.

[0042] In one embodiment, specifically: in step 5, the unlocking information includes the unlocking time, the identification method of the unlocking user, the identity information, whether the unlocking is successful, the environmental parameters around the smart lock when unlocking, and the user's operation behavior trajectory; Among them, unlocking time: accurately records the time when each unlocking operation occurs, usually accurate to seconds or even milliseconds. This helps to establish a time series and provide a time dimension basis for subsequent data analysis and abnormal behavior detection; for example, the record of "2025-01-17 15:30:25.123" can reflect the specific time when the user unlocks the lock. The user's daily unlocking habits can be analyzed based on this to determine whether there are abnormal unlocking times. For example, if the user usually unlocks the lock during the day, and an unlocking record appears in the early morning of a certain day, it may be an abnormal situation; Unlocking user identification method: records the specific identification method used by the user to unlock the door, which may be fingerprint recognition, facial recognition, iris recognition, voiceprint recognition, password input, Bluetooth unlocking, NFC card unlocking or card swiping unlocking, etc. This information allows the administrator or system to clearly know which method the user used to try to unlock the door, and facilitates statistics and analysis of the frequency and effect of different identification methods. For example, the system can find out through statistics whether the user prefers to use fingerprint recognition or password input, and the preferences of different users for different identification methods; Identity information: Contains information related to the user's identity, such as the user's unique identifier (which may be a user name, user ID, user account, etc.), in order to distinguish the unlocking operations of different users; for family users, different members have different identity information. By recording identity information, it is possible to track which family member has performed the unlocking operation, which is very important for permission management and abnormal behavior tracing; for example, for a family smart lock system with multiple users, it is possible to determine whether it is a parent or a child who has performed the unlocking operation based on the identity information; Whether the unlocking is successful: record the result of the unlocking operation, whether it is successful or failed. If the unlocking fails, the system can further analyze the cause, which may be the user entering the wrong password, identity information mismatch, or other abnormal situations; for failed unlocking operations, it can serve as an important basis for subsequent security analysis. For example, multiple consecutive failures may mean that there are abnormal attempts, perhaps someone is trying to crack the password or using incorrect identity information; Environmental parameters around the smart lock when unlocking: Environmental parameters around the smart lock are usually obtained through built-in sensors, including but not limited to: Temperature: measured by a temperature sensor, such as "25°C". The temperature may affect the performance of the smart lock or the user's operation. Extreme temperatures may cause the performance of some components to deteriorate, and may also affect the user's normal unlocking behavior. For example, the recognition effect of the fingerprint sensor may deteriorate at low temperatures. Humidity: measured using a humidity sensor, such as "60%RH". Humidity may affect the performance of the device's electronic components and can also be used as a basis for judging abnormal behavior. For example, unlocking operations under abnormally high humidity may require attention. Light intensity: obtained through light sensors, such as "500lux". Light intensity can reflect the environmental conditions at the time. For example, unlocking in an extremely dark or bright environment may be an abnormal situation, or sudden strong light exposure at night may indicate that someone is deliberately disturbing the environment. Air pressure: measured by air pressure sensors, which may affect certain smart lock functions or environmental perception. Abnormal air pressure changes may be a signal of environmental abnormalities. User operation behavior track: The user operation behavior track can be obtained in many ways, such as: Positioning information: If the smart lock is associated with a user device (such as a mobile phone), the user's location and movement trajectory before unlocking the door can be obtained through the positioning information of the mobile phone, and the user's location and movement trajectory before unlocking the door can be determined from where the user approached the door and whether the user left after unlocking the door. Proximity sensor information: Smart locks can be equipped with proximity sensors to determine the time and speed at which the user approaches the smart lock, such as whether the user stays in front of the door for a long time or approaches and leaves abnormally quickly, which may indicate different behavior patterns; Operation sequence information: records the operations performed by the user before and after unlocking the door, such as whether other operations were attempted before unlocking (such as pressing other buttons), or whether the user opened the door immediately after unlocking the door or stayed in front of the door for a while.

[0043] In one embodiment, specifically: in step 3, the permission type includes permanent permission, temporary permission, periodic permission and specific scenario permission; Among them, permanent authority is a type of authority that is valid for a long time and is usually granted to the main members of the family, such as the owner of the house or a family member who lives there for a long time. Users with permanent authority can use the smart lock to unlock the door at any time and in any environment, without being restricted by time or other conditions. For example, the owner of the house has permanent authority, and they can freely enter and exit the house during the day, at night, on weekdays or weekends through various identity recognition methods of the smart lock (such as fingerprint, facial recognition, etc.). This type of authority provides the greatest convenience for the main users and meets their daily life needs. Temporary permissions are mainly used to meet the needs of temporary visitors or short-term use. The administrator can use the management APP of the smart lock to accurately set the effective start and end time of the temporary permissions, as well as the number of unlocking times allowed. For example, when a friend comes to visit and stay for a few days, the administrator can set a temporary permission for him / her, starting from 10 am on a certain day and ending at 5 pm a few days later, and set the number of unlocking times allowed to be 10 times. In this way, visitors can use the smart lock within the specified time and number of times, and the permission will automatically expire after the time or number of times, ensuring the safety of the family and the flexibility of permission management. Periodic permissions are set based on the user's usage patterns. Administrators can set a period (such as weekly or monthly) and a specific time range, and users can use the smart lock only within the specified period. For example, for office workers with fixed working hours at home, administrators can set unlocking permissions from 7am to 9am and from 6pm to 8pm from Monday to Friday, and users cannot use the smart lock at other times. This type of permission can be customized according to the life and work patterns of different users, improving the accuracy and rationality of permission management. Specific scene permissions determine whether to grant users unlocking permissions based on specific environmental conditions or events. Smart locks can determine whether to allow users to unlock the door based on data collected by built-in environmental sensors (such as temperature and humidity sensors, light sensors, and air pressure sensors), or linkage information with other smart home devices, combined with preset scene conditions (such as temperature range, light intensity range, and specific device status). For example, when the indoor temperature is too high or too low, only specific maintenance personnel with specific scene permissions can unlock the door and enter to check the equipment. Or when the home security system detects an abnormality, only the owner or security personnel with specific permissions can open the smart lock. This type of permission increases the security and applicability of smart locks in different scenarios. The permission type combination strategy includes the combination of multiple identity identification methods, the combination of different permission types, and the use restrictions of different identity identification methods under different permission types; Combination of multiple identification methods: To improve security, the permission type combination strategy can set up a combination of multiple identification methods; for example, set up double verification, requiring users to enter a password first, and then perform fingerprint recognition to unlock; or in specific security scenarios, such as at night or after multiple consecutive unlocking failures, forcing users to use facial recognition and NFC cards to unlock at the same time; through the combination of multiple identification methods, the difficulty of illegal unlocking is increased, and the security of smart locks is improved; Combination of different permission types: Different permission types can be combined to meet more complex usage requirements. For example, while setting temporary permissions for temporary visitors, combined with specific scene permissions, it is stipulated that visitors can only use the smart lock during the day and when the indoor temperature is normal; or while setting periodic permissions for family members, according to different time periods or scenes, combined with different identity recognition methods, further refine the permission management; this combination method can flexibly adjust the permission settings according to different users and scenes, and improve the diversity and adaptability of permission management; Usage restrictions of different identification methods under different permission types: Different permission types can impose usage restrictions on different identification methods; for example, users with permanent permissions can use all identification methods; while users with temporary permissions may only be able to use specific identification methods, such as card swiping or password input, to facilitate management and control; or in periodic permissions, it is stipulated that only fingerprint recognition can be used during certain time periods, and facial recognition can be used during other time periods; such restrictions can reasonably allocate usage permissions for different identification methods according to the characteristics of the permission type and security requirements, thereby optimizing user experience and security.

[0044] In one embodiment, specifically: in step 2, the identity recognition information includes fingerprint recognition information, facial recognition information, voiceprint recognition information and iris recognition information; wherein the fingerprint recognition information is a unique feature representation of the user's fingerprint, which is collected by the fingerprint sensor of the smart lock; the facial recognition information is an image or feature representation of the user's face, which is collected by the camera of the smart lock; the voiceprint recognition information is a feature representation of the user's voice, which is collected by the microphone of the smart lock; the iris recognition information is a unique pattern feature of the user's iris, which is collected by the iris recognition sensor of the smart lock; The encryption process uses the Advanced Encryption Standard algorithm to encrypt the identity information, and uses different keys to encrypt the information of different users. Among them, the Advanced Encryption Standard algorithm (AES) is a widely used symmetric encryption algorithm, which uses the same key for encryption and decryption operations. For the encryption of identity information, different keys are used for different users to ensure that even if the information of a certain user is leaked, it will not affect the information security of other users. When storing and transmitting identity information, the above-mentioned fingerprint recognition information, facial recognition information, voiceprint recognition information and iris recognition information will be encrypted using the corresponding keys. For example, for the fingerprint information of user A, AES encryption is performed using key A, and for the facial recognition information of user B, AES encryption is performed using key B. The AES algorithm has different key lengths. (such as 128 bits, 192 bits or 256 bits), longer key lengths provide higher security; during the encryption process, the original identity information is taken as plain text and converted into ciphertext through the key and AES algorithm. The ciphertext looks like an unordered sequence of characters and the user's original information cannot be directly identified; only by using the corresponding key can the ciphertext be restored to the original identity information; when these encrypted identity information is stored in the local storage module, cloud server or edge computing node of the smart lock, even if these storage devices are illegally accessed, since the information is encrypted, it is difficult for attackers to obtain the user's real identity information, thereby ensuring the security and privacy of the user's identity information; at the same time, during the information transmission process, the encrypted information can also prevent the information from being stolen and tampered with, thereby improving the security of the entire system.

[0045] In summary, the multimodal permission management and record tracing method for a smart lock provided by an embodiment of the present invention organically combines functions such as identity recognition, permission management, operation records, security verification and emergency handling through multi-step collaborative work to form a complete system, thereby improving the security, convenience and manageability of smart locks, solving many problems existing in traditional smart locks, and providing a more comprehensive and intelligent solution for home security.

[0046] Figure 4 Schematic diagram of the functional modules of a smart lock multi-modal authority management and record tracing system according to an embodiment of the present application. Figure 4 As shown, a smart lock multimodal permission management and record tracing system includes: a smart lock device module, a management APP module, a cloud server module, an edge computing node module, a multi-factor authentication module, a big data analysis module and an emergency processing module; The smart lock device module is used to obtain the user's identity information through multiple built-in identity recognition modes, and perform a preliminary match on the obtained identity information in the local storage module. If the match fails, a remote matching verification request is sent to the cloud server module and the edge computing node module through a secure channel for remote matching verification; Management APP module, used to provide a user operation interface, support users to enter a variety of identity information through the module, and encrypt the entered information and send it to the smart lock device module, cloud server module and edge computing node module; The cloud server module is used to receive and store the identity information, permission information, and unlocking records uploaded by the smart lock device module; provide data access services for the management APP module to obtain unlocking records within a specified time period; The edge computing node module is used to receive the remote matching verification request sent by the smart lock device module and assist in matching and verifying the identity information; Multi-factor authentication module, which is used to obtain historical behavior patterns, geographic location information and device fingerprint information, and combine identity recognition information to make comprehensive judgments based on preset rules and algorithms; The big data analysis module is used to obtain historical unlocking record data from the cloud server module and the edge computing node module, and use machine learning algorithms to train historical unlocking records, build user behavior models, and identify potential security risks and abnormal behavior patterns; The emergency processing module is used to automatically trigger the emergency mode when the smart lock device module encounters a network failure, system failure or is attacked by malicious attacks.

[0047] In one embodiment, specifically: the local storage module in the smart lock device module uses encrypted storage technology to encrypt the stored identity information, authority information and unlocking records, and the local storage module has data backup and recovery functions; the management APP module has a user behavior analysis function, which is used to analyze the user's operating habits and preferences based on the user's operation records of the smart lock.

[0048] In one embodiment, specifically: the cloud server module adopts a distributed storage architecture to store data in multiple storage nodes; in emergency mode, the emergency processing module sends the operating status and emergency situation of the smart lock to the administrator in real time via text messages or push notifications.

[0049] In summary, an embodiment of the present invention provides a smart lock multimodal permission management and record tracing system, which obtains and matches user identity identification information through a smart lock device module, manages the APP module to input and encrypt the transmission of user identity identification information, stores relevant data and provides data access for the management APP, and assists in remote matching and verification by an edge computing node module. A multi-factor authentication module comprehensively determines the user identity, a big data analysis module constructs a user behavior model to identify potential risks, and an emergency processing module triggers an emergency mode in an abnormality. Specifically, the local storage module of the smart lock device module adopts encrypted storage and has backup and recovery functions, the management APP module can analyze user operating habits and preferences, the cloud server module adopts a distributed storage architecture, and the emergency processing module notifies the administrator of the smart lock operating status and emergency situations by SMS or push in the emergency mode, thereby comprehensively improving the security, convenience and management efficiency of the smart lock system.

[0050] For other details about the technical solutions for implementing each module in the smart lock multimodal permission management and record tracing system in the above embodiment, please refer to the description of the smart lock multimodal permission management and record tracing method in the above embodiment, which will not be repeated here.

[0051] It should be noted that each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same or similar parts between the embodiments can be referred to each other. For system embodiments, since they are basically similar to method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0052] The basic principles of the present disclosure are described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, effects, etc. mentioned in the present disclosure are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. are required by each embodiment of the present disclosure. In addition, the specific details disclosed above are only for the purpose of illustration and ease of understanding, and are not limitations. The above details do not limit the present disclosure to the necessity of adopting the above specific details to be implemented.

[0053] In the present disclosure, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. The block diagrams of the devices, devices, equipment, and systems involved in the present disclosure are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagram. As will be appreciated by those skilled in the art, these devices, devices, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open words, referring to "including but not limited to", and can be used interchangeably with them. The words "or" and "and" used here refer to the words "and / or" and can be used interchangeably with them, unless the context clearly indicates otherwise. The words "such as" used here refer to the phrase "such as but not limited to", and can be used interchangeably with them.

[0054] Additionally, as used herein, "or" used in a list of items beginning with "at least one" indicates a separate list, so that, for example, a list of "at least one of A, B, or C" means A or B or C, or AB or AC or BC, or ABC (i.e., A and B and C). Furthermore, the word "exemplary" does not mean that the example described is preferred or better than other examples.

[0055] It should also be noted that in the system and method of the present disclosure, each component or each step can be decomposed and / or recombined. Such decomposition and / or recombination should be regarded as equivalent solutions of the present disclosure.

[0056] Various changes, substitutions, and modifications of the techniques described herein may be made without departing from the teachings defined by the appended claims. Furthermore, the scope of the claims of the present disclosure is not limited to the specific aspects of the processes, machines, manufactures, compositions of events, means, methods, and actions described above. Currently existing or later to be developed processes, machines, manufactures, compositions of events, means, methods, or actions that perform substantially the same functions or achieve substantially the same results as the corresponding aspects described herein may be utilized. Thus, the appended claims include such processes, machines, manufactures, compositions of events, means, methods, or actions within their scope.

[0057] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present disclosure. Therefore, the present disclosure is not intended to be limited to the aspects shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

[0058] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present disclosure to the forms disclosed herein. Although multiple example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

Claims

1. A smart lock multi-modal permission management and record tracing method, characterized in that: The following steps are involved: Step 1: Get a smart lock, initialize the smart lock, connect it to the home LAN, and establish a secure communication connection with the cloud server; Step 2: Enter various identity information through the management APP of the smart lock, encrypt it, and then upload it to the local storage module of the smart lock, the cloud server, and the edge computing node; Step 3: Assign permission types to different users based on the management APP and formulate a permission type combination strategy; Step 4: When the user unlocks the lock, the smart lock obtains the corresponding identification information according to the identification method selected by the user, and performs a preliminary match in the local storage module. If the local match fails, a request is sent to the cloud server and edge computing node through a secure channel for remote matching verification; Step 5: The smart lock automatically records the unlocking information of each unlocking operation and stores it in the local storage module of the smart lock, and synchronizes it to the cloud server and edge computing node in real time; Step 6: Obtain unlocking records from cloud servers or edge computing nodes through the management app, and use big data analysis technology to conduct in-depth mining and analysis to identify potential security risks and abnormal behavior patterns; Step 7. When the smart lock encounters a network failure, system failure or is attacked by a malicious attack, it automatically switches to emergency mode and performs a limited number of unlocking operations in emergency mode based on the locally stored permission information and backup identity recognition methods.

2. According to claim 1, a smart lock multi-modal authority management and record tracing method is characterized by: In step six, the big data analysis technology uses a machine learning algorithm to train historical unlocking records and build a user behavior model; The method for constructing a user behavior model comprises the following steps: Step 601: Obtain historical unlocking record data from the cloud server and the edge computing node, and pre-process the historical unlocking record data; Step 602: extract valuable feature data from the pre-processed historical unlocking record data, and divide the feature data into a training set, a verification set, and a test set; Step 603: Select a decision tree, support vector machine or deep learning neural network in the machine learning algorithm as the basic architecture of the model; Step 604: Use the training set and the validation set as input, and use normal behavior and abnormal behavior as output labels to train the model; Step 605: Use the test data set to evaluate the trained model, calculate the accuracy, recall rate, and F1 value, and optimize and adjust the model based on the evaluation results.

3. According to claim 1, a smart lock multi-modal authority management and record tracing method is characterized by: In step 4, during the remote matching verification process, a multi-factor authentication mechanism is adopted. The specific steps are as follows: Step 401: Establish user behavior pattern characteristics by analyzing the user's previous unlocking time, unlocking frequency, and common identity recognition methods, and obtain the user's historical behavior pattern; Step 402: Acquire the user's current geographic location information by cooperating with the positioning function of the user's device; Step 403: Obtain device fingerprint information of the user operating the device by collecting the hardware information and software information of the device; Step 404: Combine historical behavior patterns, geographic location information, and device fingerprint information with identity information to make a comprehensive judgment based on preset rules and algorithms; Step 405: If all verification conditions are met, the smart lock performs an unlocking operation; If the verification fails, unlocking will be refused and detailed information of this abnormal unlocking attempt will be recorded.

4. According to claim 1, a smart lock multi-modal authority management and record tracing method is characterized by: In step one, the smart lock supports multiple identity recognition modes, including fingerprint recognition, facial recognition, iris recognition, voiceprint recognition, password input, Bluetooth unlocking, NFC card unlocking and card swiping unlocking; the secure communication connection uses encryption protocols SSL or TLS.

5. According to claim 1, a smart lock multi-modal authority management and record tracing method is characterized by: In step five, the unlocking information includes the unlocking time, the identification method of the unlocking user, the identity information, whether the unlocking is successful, the environmental parameters around the smart lock when unlocking, and the user's operation behavior trajectory.

6. According to claim 1, a smart lock multi-modal authority management and record tracing method is characterized by: In step 3, the permission types include permanent permission, temporary permission, periodic permission and specific scenario permission; The permission type combination strategy includes the combination of multiple identity identification methods, the combination of different permission types, and the usage restrictions of different identity identification methods under different permission types.

7. According to claim 1, a smart lock multi-modal authority management and record tracing method is characterized by: In step 2, the identity recognition information includes fingerprint recognition information, facial recognition information, voiceprint recognition information and iris recognition information; the encryption process uses an advanced encryption standard algorithm to encrypt the identity recognition information, and uses different keys to encrypt information of different users.

8. A smart lock multimodal authority management and record tracing system, applied to a smart lock multimodal authority management and record tracing method according to any one of claims 1 to 7, characterized in that: include: Smart lock device module, management APP module, cloud server module, edge computing node module, multi-factor authentication module, big data analysis module and emergency response module; The smart lock device module is used to obtain the user's identity information through the built-in multiple identity recognition modes, and perform a preliminary match on the obtained identity information in the local storage module. If the match fails, a remote matching verification request is sent to the cloud server module and the edge computing node module through a secure channel for remote matching verification; The management APP module is used to provide a user operation interface, support users to enter a variety of identity information through the module, and encrypt the entered information and send it to the smart lock device module, cloud server module and edge computing node module; The cloud server module is used to receive and store the identity information, authority information, and unlocking records uploaded by the smart lock device module; provide data access services for the management APP module to obtain unlocking records within a specified time period; The edge computing node module is used to receive a remote matching verification request sent by the smart lock device module and assist in matching verification of identity information; The multi-factor authentication module is used to obtain historical behavior patterns, geographic location information and device fingerprint information, and make comprehensive judgments based on preset rules and algorithms in combination with identity recognition information; The big data analysis module is used to obtain historical unlocking record data from the cloud server module and the edge computing node module, and use machine learning algorithms to train historical unlocking records, build user behavior models, and identify potential security risks and abnormal behavior patterns; The emergency processing module is used to automatically trigger the emergency mode when the smart lock device module encounters a network failure, a system failure or is attacked by a malicious attack.

9. The smart lock multimodal authority management and record tracing system according to claim 8, characterized in that: The local storage module in the smart lock device module adopts encrypted storage technology to encrypt the stored identity information, authority information and unlocking records. The local storage module has data backup and recovery functions; the management APP module has a user behavior analysis function, which is used to analyze the user's operating habits and preferences based on the user's operation records of the smart lock.

10. The smart lock multi-modal authority management and record tracing system according to claim 8, characterized in that: The cloud server module adopts a distributed storage architecture to store data in multiple storage nodes in a dispersed manner; in emergency mode, the emergency processing module sends the operating status and emergency situation of the smart lock to the administrator in real time via text messages or push notifications.

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