Intelligent door lock and security management method, device, system, medium and product thereof

Abstract

The application relates to an intelligent door lock and a safety control method, device, system, medium and product thereof. The safety control method comprises the following steps: in response to an unlocking request, obtaining state information of the intelligent door lock; based on a preset analysis model, determining an unlocking behavior category according to the state information; in the case that the unlocking behavior category is an abnormal unlocking behavior, controlling the intelligent door lock to perform a false unlocking operation, and feeding back the state information to the cloud, and the cloud is used for performing a preset operation based on the state information. By adopting the method, malicious attack behaviors can be actively defended, and the state information is fed back to the cloud, so that the cloud can perform a preset operation according to the abnormal unlocking behavior. The method realizes the differentiation of false touch behaviors and abnormal unlocking behaviors, reduces false positives, and improves user experience.

Description

Technical Field

[0001] This application relates to the field of smart door lock technology, and in particular to a smart door lock and its security control method, device, system, medium and product. Background Technology

[0002] In the wave of rapid development of Internet of Things (IoT) technology, smart door locks have been widely used in homes, offices and various life scenarios due to their outstanding convenience and intelligent advantages.

[0003] In related technologies, smart locks can provide immediate protection when damaged through alarm deterrence and locking mechanisms. However, current smart lock products still have shortcomings in identifying malicious damage, which can easily lead to false alarms or missed alarms, directly affecting the user experience. Summary of the Invention

[0004] Therefore, it is necessary to provide an intelligent door lock and its security management method, device, system, medium and product to address the above-mentioned technical problems.

[0005] Firstly, this application provides a security control method for a smart door lock, the security control method comprising:

[0006] In response to an unlocking request, obtain the status information of the smart lock;

[0007] Based on a preset analysis model, the unlocking behavior category is determined according to the state information;

[0008] If the unlocking behavior is classified as an abnormal unlocking behavior, the smart door lock is controlled to perform a fake unlocking operation, and the status information is fed back to the cloud. The cloud is used to perform preset operations based on the status information.

[0009] In some embodiments, controlling the smart lock to perform a fake unlocking operation when the unlocking behavior category is abnormal unlocking behavior includes:

[0010] If the behavior category is abnormal unlocking behavior, play an unlocking sound but do not perform an unlocking operation.

[0011] In some embodiments, feeding back the status information to the cloud includes:

[0012] The status information is encrypted to obtain encrypted information;

[0013] The encrypted information is fed back to the cloud, which is used to decrypt the encrypted information after receiving it to obtain the status information.

[0014] In some embodiments, the preset operation includes analyzing the status information and issuing update information based on the analysis results. The security control method further includes:

[0015] The preset analysis model is updated based on the update information sent from the cloud.

[0016] Secondly, this application provides a security control device for a smart door lock, the security control device comprising:

[0017] The acquisition module is used to acquire the status information of the smart door lock in response to the unlocking request;

[0018] The determination module is used to determine the unlocking behavior category based on the state information and a preset analysis model.

[0019] The first execution module is used to control the smart door lock to perform a fake unlocking operation when the unlocking behavior category is abnormal unlocking behavior, and to feed back the status information to the cloud, whereby the cloud is used to perform a preset operation based on the status information.

[0020] Thirdly, this application provides a security management system, including a smart door lock and a cloud that can implement the security management method of any of the above embodiments.

[0021] In some embodiments, the cloud is used to perform cluster analysis based on the status information to generate profile information; and to generate update information based on the profile information, wherein the update information is used to update the preset analysis model.

[0022] Fourthly, this application provides an intelligent door lock, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the security control method of any of the above embodiments.

[0023] Fifthly, this application provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the security control method of any of the above embodiments.

[0024] Sixthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the security control method of any of the above embodiments.

[0025] This application discloses a security management method, security management device, smart lock, security management system, computer-scaled storage medium, and computer program product for smart locks. Based on the smart lock's status information, a preset analysis model categorizes the behavior. When the behavior is classified as an abnormal unlocking action, a false unlocking operation is executed to proactively defend against malicious attacks. The status information is then fed back to the cloud, allowing the cloud to execute preset operations based on the abnormal unlocking behavior. This effectively distinguishes between accidental touches and abnormal unlocking actions, reducing false alarms and improving user experience. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 This is a flowchart illustrating a security control method for a smart door lock in one embodiment;

[0028] Figure 2 This is a flowchart illustrating the execution of a fake unlocking operation when the behavior category is abnormal unlocking behavior, as shown in one embodiment.

[0029] Figure 3 This is a schematic diagram illustrating the process of feeding back status information to the cloud in one embodiment;

[0030] Figure 4 This is a flowchart illustrating the security control method in another embodiment;

[0031] Figure 5 This is a structural block diagram of the security control device of a smart door lock in one embodiment;

[0032] Figure 6 This is a structural block diagram of a security control system in one embodiment;

[0033] Figure 7 This is a structural block diagram of a smart door lock in one embodiment. Detailed Implementation

[0034] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0035] Before describing the embodiments of the present invention, the related technologies and their existing problems will be further explained:

[0036] In the wave of rapid development of IoT technology, smart locks, with their outstanding convenience and intelligent advantages, have moved from concept to household use, becoming a common feature in various living scenarios such as homes, offices, hotels, and short-term rental apartments. Through fingerprint, password, and remote control via mobile phone, they greatly improve the convenience of entry and exit and management efficiency, making them an important part of modern smart living. Specifically regarding security technology, current smart locks can activate a relatively mature proactive defense mechanism when subjected to physical damage or abnormal attempts: high-decibel audible and visual alarms provide on-site deterrence and neighborly reminders, while remote application push notifications instantly reach the homeowner; simultaneously, a locking mechanism is triggered, rejecting all unlocking attempts within a set time, physically blocking further intrusion. These measures form the basis for in-process protection and post-incident traceability capabilities.

[0037] However, as applications become more widespread, technical bottlenecks are becoming increasingly apparent. One of the core issues is the insufficient ability to accurately identify attack intent. Existing systems largely rely on simple threshold triggers, lacking in-depth analysis of behavioral scenarios. This leads to both false positives and false negatives. Frequent false positives can cause user security fatigue, causing them to gradually ignore alerts and severely reducing trust and user experience; while a single critical false negative can pose a substantial security risk.

[0038] Therefore, the key to improving the reliability and user experience of smart door locks lies in endowing them with "intelligence" that is closer to human judgment—that is, more accurately distinguishing between accidental interference and malicious damage through multi-sensor fusion. This is not only the direction of technological evolution, but also the inevitable path for them to move from "convenient tools" to "trustworthy security stewards."

[0039] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and their variations, used in this application are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0040] To solve the above technical problems, such as Figure 1 As shown, this application provides a security control method for a smart door lock, which includes steps 102 to 106. Wherein:

[0041] Step 102: In response to the unlocking request, obtain the status information of the smart lock.

[0042] The unlock request allows the user to control the smart lock. In other words, when a user unlocks the smart lock, the microcontroller receives the unlock request and obtains the smart lock's status information.

[0043] Smart locks incorporate multiple sensors to collect status information. These sensors include torque sensors, current sensors, and angle sensors.

[0044] In other words, the status information of a smart lock can be obtained by acquiring sensor data. This status information includes data such as the current, voltage, torque, angle, button input, and / or time interval of the smart lock when the user attempts to unlock it.

[0045] Step 104: Based on the preset analysis model, determine the unlocking behavior category according to the status information.

[0046] The preset analysis model includes a pre-trained artificial intelligence model. This model can be used to classify state information and output the unlocking behavior category.

[0047] Furthermore, the preset analysis model can be a pre-trained lightweight artificial intelligence model, so as to determine the unlocking behavior category without consuming too much computing power, with low edge computing resource consumption and minimal impact on the power consumption of the door lock. In one example, the preset analysis model can be a TinyML (Tiny Machine Learning) model.

[0048] Step 106: If the unlocking behavior is classified as abnormal unlocking behavior, control the smart door lock to perform a fake unlocking operation and feed the status information back to the cloud. The cloud will then use the status information to perform preset operations.

[0049] Abnormal unlocking behaviors include remote control, forged unlocking commands, forged fingerprint unlocking, forced destruction, and high-frequency trial and error.

[0050] If the unlocking behavior is determined to be abnormal, the smart lock is considered to be under malicious attack and cannot perform unlocking operations. To mislead the attacker and give the smart lock user time to react, a fake unlocking operation can be performed. Simultaneously, the status information is fed back to the cloud, allowing the cloud to perform preset operations based on this information.

[0051] Furthermore, abnormal unlocking behavior can be sent to the user via the smart lock's built-in communication module or the cloud to remind the user to handle the abnormal unlocking behavior.

[0052] In some implementations, the unlocking behavior category may include normal unlocking behavior. If the unlocking behavior is normal, the regular unlocking operation is performed. If the unlocking behavior category is abnormal, step 106 is executed.

[0053] In one embodiment, the solution of this application can also be used to supplement the identification of conventional malicious attacks. Specifically, when a conventional malicious attack is identified, status information is acquired, and based on a preset analysis model, the unlocking behavior category is determined according to the status information. If the unlocking behavior category is determined to be abnormal unlocking behavior, the smart door lock is controlled to perform a fake unlocking operation, and the status information is fed back to the cloud, where it is used to perform preset operations based on the status information. If the unlocking behavior category is determined to be accidental unlocking behavior, a normal unlocking operation is performed. Here, accidental unlocking behavior can be understood as a user accidentally touching the door while unlocking, causing the conventional malicious attack identification to mistakenly identify this unlocking control as a malicious attack.

[0054] In this embodiment, a preset analysis model categorizes the smart lock's status information. If the behavior is classified as an abnormal unlocking action, a fake unlocking operation is executed to proactively defend against malicious attacks. The status information is then fed back to the cloud, allowing the cloud to execute preset actions based on the abnormal unlocking behavior. This effectively distinguishes between accidental touches and abnormal unlocking actions, reducing false alarms and improving the user experience.

[0055] In some embodiments, such as Figure 2 As shown, step 106, when the unlocking behavior category is abnormal unlocking behavior, controls the smart door lock to perform a fake unlocking operation, including step 202. Wherein:

[0056] Step 202: If the behavior category is abnormal unlocking behavior, play the unlocking sound but do not perform the unlocking operation.

[0057] Specifically, a fake unlocking operation includes playing an unlocking sound without actually unlocking the door. In other words, when the behavior is classified as an abnormal unlocking behavior, the smart lock can be controlled to play an unlocking sound without performing the unlocking operation, in order to confuse attackers.

[0058] In one embodiment, the spoofing operation also includes a delayed unlocking operation.

[0059] In some implementations, different abnormal unlocking behaviors correspond to different spoofing operations. That is, the behavior category determined based on a preset analysis model can be a specific behavior category, and abnormal unlocking behaviors include multiple specific behavior categories. When a specific behavior category belongs to abnormal unlocking behavior, the spoofing operation corresponding to that specific behavior category is executed.

[0060] In another instance, if the analyzed behavior is normal unlocking behavior, the motor of the smart lock is controlled to perform normal unlocking operation; if it is abnormal unlocking behavior, a fake unlocking prompt sound or action is triggered to confuse the attacker, and the event information is sent to the cloud by the communication module inside the smart lock.

[0061] In this embodiment, by performing a fake unlocking operation—playing an unlocking sound but not actually unlocking—when the behavior category is abnormal unlocking behavior, attackers can be misled, proactive defense can be achieved, and users can be given reaction time.

[0062] In some embodiments, after acquiring the state information, the state information is preprocessed by filtering, normalization, and feature extraction to obtain a state feature vector. The state feature vector is then input into a pre-defined TinyML model to classify the feature vector and output the behavior category.

[0063] In some embodiments, such as Figure 3 As shown, in step 106, the status information is fed back to the cloud, including steps 302 and 304. Wherein:

[0064] Step 302: Encrypt the status information to obtain encrypted information;

[0065] Step 304: Send the encrypted information to the cloud. The cloud will decrypt the encrypted information upon receipt and obtain the status information.

[0066] Specifically, to ensure communication security, the status information is encrypted before being sent to the cloud, and this encrypted information is then sent back to the cloud. Upon receiving the encrypted information, the cloud performs a decryption operation to retrieve the status information.

[0067] The encryption methods include TLS (Transport Layer Security), SSL (Secure Sockets Layer), and HTTPS (Hypertext Transfer Protocol Secure).

[0068] In one embodiment, if the unlocking behavior is classified as an abnormal unlocking behavior, the event log of the abnormal unlocking behavior is fed back to the cloud. The event log includes fields such as the smart lock device's identifier ID, timestamp, collected sensor feature vectors, determined behavior category, and encrypted random number, for cloud-based analysis and tracing.

[0069] In another embodiment, the event log, feature vector, and determination result are encrypted and sent to the cloud using the TLS security protocol. The feature vector is obtained after preprocessing the state information. The determination result is the behavior category. The event log contains data related to this abnormal unlocking behavior.

[0070] In this embodiment, encrypted information is obtained by encrypting the status information, and the cloud can only obtain the status information after decrypting the encrypted information, thus realizing secure transmission of status information and secure communication between the smart lock and the cloud.

[0071] In some embodiments, such as Figure 4 As shown, the preset operation includes analyzing the status information and issuing update information based on the analysis results. The security control method also includes step 402. Wherein:

[0072] Step 402: Update the preset analysis model according to the update information sent from the cloud.

[0073] Specifically, the cloud can analyze the status information uploaded by the smart lock to determine whether the current abnormal unlocking operation belongs to a previously recorded abnormal unlocking operation or whether it is a new abnormal unlocking operation, and obtain the analysis results. The cloud can also determine whether the preset analysis model needs to be updated based on the analysis results.

[0074] If it is determined that the preset analysis model needs to be updated, the cloud determines the update information based on the analysis results and sends it to the smart lock.

[0075] Upon receiving update information, the smart lock updates and upgrades its preset analysis model accordingly. Furthermore, the update includes parameters such as the sampling window length, feature dimensions, threshold, and neural network weights. Updating these parameters improves the detection accuracy of abnormal unlocking operations.

[0076] In one embodiment, the cloud analyzes the status information to determine if any new abnormal unlocking operations exist. If so, it determines that the preset analysis model needs to be updated or the thresholds in the preset analysis model need to be adjusted, and at the same time, a new false unlocking behavior corresponding to the new abnormal unlocking operation is formulated.

[0077] In this embodiment, the status information is analyzed by the cloud, and update information is sent out based on the analysis results. The smart door lock is controlled to update the preset analysis model according to the update information sent by the cloud, thereby realizing the update and upgrade of the preset analysis model and the monitoring cycle between the smart door lock and the preset analysis model, forming a closed-loop optimization.

[0078] In some embodiments, such as Figure 5As shown, this application provides a security management system 500, including a smart door lock 502 and a cloud 504 capable of implementing the security management method of any of the above embodiments.

[0079] Specifically, the security management system adopts an edge-cloud collaborative system architecture. The edge device is a smart door lock. The smart door lock may include components such as a microprocessor, multiple sensors, and communication modules. The cloud service uses a microservice architecture, consisting of data aggregation and analysis services, a threat profiling database, model update services, and upgrade push services. Both parties communicate through a secure channel to jointly complete the detection of abnormal unlocking behavior, response to false unlocking, and iteration of protection strategies.

[0080] It should be noted that one cloud platform can communicate with multiple smart locks. In other words, one cloud platform can be used to manage updates for multiple smart locks.

[0081] In some embodiments, the cloud is also used to manage the firmware version and preset analysis model version of the smart lock device, and to push update packages to the corresponding smart lock according to the device model and serial number to ensure secure upgrades and version rollbacks.

[0082] In some embodiments, the cloud is used to perform cluster analysis based on the status information to generate profile information; and to generate update information based on the profile information, the update information being used to update the preset analysis model.

[0083] Specifically, the clustering algorithms used in the cloud include K-Means clustering. By performing cluster analysis on the state information, attack profiles of malicious attacks are generated to identify whether a new attack pattern has emerged. If a new attack pattern is identified, update information for updating the preset analysis model is generated based on the profile information.

[0084] The profile information includes the average feature vector of each behavior type, the number of events, the severity, and the applicable deception strategies, which facilitates model updates. When a new attack behavior is identified, the profile information database can be updated based on the identified new profile information, providing a basis for subsequent analysis.

[0085] In one embodiment, the cloud receives and decrypts abnormal unlocking behavior data, such as status information, verifies the signature, and stores it in a cloud database. Cluster analysis is then performed on this data in conjunction with historical records in the database. Attack profile information is generated based on the cluster analysis results, and the system determines whether model parameters need adjustment or a new deception strategy needs to be added. If it is determined that model parameters need adjustment or a new deception strategy needs to be added, update information for updating the preset analysis model is generated.

[0086] In another embodiment, the update information includes the firmware version number or preset analysis model version number of the smart lock, a list of applicable smart lock device models, hash check value, and signature information, which are used for verification and upgrades on the smart lock device side.

[0087] Compared to conventional passive alarm solutions, the security control system of this application can immediately identify abnormal unlocking behaviors such as forced lock picking or high-frequency trial and error during an attack, and execute deceptive responses such as fake unlocking operations locally to delay the attack. Simultaneously, abnormal unlocking events are encrypted and uploaded to the cloud. The cloud uses cross-device data aggregation and analysis to construct threat profile information and automatically pushes updated information, enabling smart locks to adaptively update and upgrade, thus improving the adaptability of the security control system.

[0088] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0089] Based on the same inventive concept, this application also provides a security control device for implementing the security control method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more security control device embodiments provided below can be found in the limitations of the security control method described above, and will not be repeated here.

[0090] In one exemplary embodiment, such as Figure 6 As shown, this application provides a security control device 600 for a smart door lock. The security control device 600 includes an acquisition module 602, a determination module 604, and a first execution module 606. Wherein:

[0091] The acquisition module 602 is used to acquire the status information of the smart door lock in response to the unlocking request.

[0092] The determination module 604 is used to determine the unlocking behavior category based on the status information and a preset analysis model.

[0093] The first execution module 606 is used to control the smart door lock to perform a fake unlocking operation when the unlocking behavior is classified as an abnormal unlocking behavior, and to feed back the status information to the cloud. The cloud is used to perform preset operations based on the status information.

[0094] Each module in the aforementioned security control device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of the smart lock in hardware form or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0095] In one exemplary embodiment, a smart lock is provided, which can be a terminal, and its internal structure diagram can be as follows. Figure 7 As shown, the smart lock includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a security management method. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the smart lock can be a touch layer covering the display screen or a button set on the smart lock shell.

[0096] Those skilled in the art will understand that Figure 7 The structure shown is merely a block diagram of a portion of the structure related to the solution of this application and does not constitute a limitation on the smart lock to which the solution of this application is applied. A specific smart lock may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0097] In one exemplary embodiment, a smart lock is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps included in any of the foregoing method embodiments.

[0098] In one embodiment, a readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps included in any of the foregoing security control method embodiments.

[0099] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps included in any of the foregoing security management method embodiments.

[0100] It should be noted that the solutions to the problems provided by the smart locks, security management systems, computer-readable storage media or computer programs described above are similar to the solutions described in the methods above. Therefore, the specific limitations of one or more embodiments of smart locks, security management systems, computer-readable storage media or computer programs provided above can be found in the limitations of the security management methods above, and will not be repeated here.

[0101] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0102] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. References to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0103] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0104] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A security control method for a smart door lock, characterized in that, The security control method includes: In response to an unlocking request, obtain the status information of the smart lock; Based on a preset analysis model, the unlocking behavior category is determined according to the state information; If the unlocking behavior is classified as an abnormal unlocking behavior, the smart door lock is controlled to perform a fake unlocking operation, and the status information is fed back to the cloud. The cloud is used to perform preset operations based on the status information.

2. The security control method according to claim 1, characterized in that, When the unlocking behavior is classified as an abnormal unlocking behavior, controlling the smart lock to perform a fake unlocking operation includes: If the behavior category is abnormal unlocking behavior, play an unlocking sound but do not perform an unlocking operation.

3. The security control method according to claim 1, characterized in that, The step of feeding back the status information to the cloud includes: The status information is encrypted to obtain encrypted information; The encrypted information is fed back to the cloud, which is used to decrypt the encrypted information after receiving it to obtain the status information.

4. The security control method according to claim 1, characterized in that, The preset operation includes analyzing the status information and issuing update information based on the analysis results. The security control method also includes: The preset analysis model is updated based on the update information sent from the cloud.

5. A security control device for an intelligent door lock, characterized in that, The safety control device includes: The acquisition module is used to acquire the status information of the smart door lock in response to the unlocking request; The determination module is used to determine the unlocking behavior category based on the state information and a preset analysis model. The first execution module is used to control the smart door lock to perform a fake unlocking operation when the unlocking behavior category is abnormal unlocking behavior, and to feed back the status information to the cloud, whereby the cloud is used to perform a preset operation based on the status information.

6. A security control system, characterized in that, This includes smart locks and cloud computing that can implement the security management method described in any one of claims 1-4.

7. The security control system according to claim 6, characterized in that, The cloud platform is used to perform cluster analysis based on the status information to generate profile information; And, based on the portrait information, generate update information, which is used to update the preset analysis model.

8. A smart door lock, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the security control method according to any one of claims 1 to 4.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the security control method according to any one of claims 1 to 4.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the security control method according to any one of claims 1 to 4.

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