CN117315818A - Intelligent door lock alarm control method and device and electronic equipment - Google Patents

Intelligent door lock alarm control method and device and electronic equipment Download PDF

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Publication number
CN117315818A
CN117315818A CN202311103650.4A CN202311103650A CN117315818A CN 117315818 A CN117315818 A CN 117315818A CN 202311103650 A CN202311103650 A CN 202311103650A CN 117315818 A CN117315818 A CN 117315818A
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China
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user
alarm
door lock
intelligent door
data
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CN202311103650.4A
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Inventor
詹志华
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SHENZHEN ZAIFENGDA TECHNOLOGY CO LTD
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SHENZHEN ZAIFENGDA TECHNOLOGY CO LTD
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Priority to CN202311103650.4A priority Critical patent/CN117315818A/en
Publication of CN117315818A publication Critical patent/CN117315818A/en
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    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
    • G07C9/00—Individual registration on entry or exit
    • G07C9/00174—Electronically operated locks; Circuits therefor; Nonmechanical keys therefor, e.g. passive or active electrical keys or other data carriers without mechanical keys
    • G07C9/00896—Electronically operated locks; Circuits therefor; Nonmechanical keys therefor, e.g. passive or active electrical keys or other data carriers without mechanical keys specially adapted for particular uses
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/94—Hardware or software architectures specially adapted for image or video understanding
    • G06V10/95—Hardware or software architectures specially adapted for image or video understanding structured as a network, e.g. client-server architectures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/168—Feature extraction; Face representation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/172—Classification, e.g. identification
    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
    • G07C9/00—Individual registration on entry or exit
    • G07C9/00174—Electronically operated locks; Circuits therefor; Nonmechanical keys therefor, e.g. passive or active electrical keys or other data carriers without mechanical keys
    • G07C9/00563—Electronically operated locks; Circuits therefor; Nonmechanical keys therefor, e.g. passive or active electrical keys or other data carriers without mechanical keys using personal physical data of the operator, e.g. finger prints, retinal images, voicepatterns
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B13/00—Burglar, theft or intruder alarms
    • G08B13/02—Mechanical actuation
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B13/00—Burglar, theft or intruder alarms
    • G08B13/18—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength
    • G08B13/189—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems
    • G08B13/194—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems
    • G08B13/196—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems using television cameras
    • G08B13/19602—Image analysis to detect motion of the intruder, e.g. by frame subtraction
    • G08B13/19613—Recognition of a predetermined image pattern or behaviour pattern indicating theft or intrusion
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Health & Medical Sciences (AREA)
  • Oral & Maxillofacial Surgery (AREA)
  • Multimedia (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • General Health & Medical Sciences (AREA)
  • Human Computer Interaction (AREA)
  • Software Systems (AREA)
  • Alarm Systems (AREA)

Abstract

The application provides an intelligent door lock alarm control method and device and electronic equipment, and relates to the technical field of data processing. In the method, an intelligent door lock acquires user behavior data; inputting user behavior data into a preset recognition model to obtain an output result; if the output result is abnormal, acquiring facial feature data of the user; and determining an alarm mode according to the facial feature data of the user so as to execute alarm operation according to the alarm mode. By means of the technical scheme, the intelligent door lock is convenient to alarm in time.

Description

Intelligent door lock alarm control method and device and electronic equipment
Technical Field
The application relates to the technical field of data processing, in particular to an intelligent door lock alarm control method, an intelligent door lock alarm control device and electronic equipment.
Background
The intelligent door lock is an improved lock which is different from the traditional mechanical lock and is more intelligent and simplified in the aspects of user safety, identification and manageability.
At present, the common unlocking mode of the intelligent door lock is to unlock by adopting modes such as digital codes, fingerprints or facial recognition, and the like, and the unlocking mode is simple and convenient to operate, high in unlocking efficiency and convenient for users to use. However, when a person unrelated to the user of the intelligent door lock wants to perform unlocking, an extreme means is adopted for the intelligent door lock so as to achieve the purpose of theft. In the related art, the intelligent door lock cannot realize intelligent identification on extreme means such as theft and the like, so that an alarm cannot be given in time.
Therefore, an intelligent door lock alarm control method, an intelligent door lock alarm control device and electronic equipment are urgently needed.
Disclosure of Invention
The application provides an intelligent door lock alarm control method, an intelligent door lock alarm control device and electronic equipment, and the intelligent door lock can alarm in time.
In a first aspect of the present application, an intelligent door lock alarm control method is provided, applied to an intelligent door lock, and the method includes: acquiring user behavior data; inputting the user behavior data into a preset recognition model to obtain an output result; if the output result is abnormal, acquiring facial feature data of the user; and determining an alarm mode according to the facial feature data of the user so as to execute alarm operation according to the alarm mode.
By adopting the technical scheme, the intelligent door lock firstly acquires the user behavior data, and then inputs the user behavior data into the preset recognition model, so that an output result is obtained. When the intelligent door lock determines that the output result is abnormal, facial feature data of the user can be acquired. Then, the intelligent door lock determines an alarm mode according to the facial feature data of the user, so that an alarm is timely carried out according to the alarm mode.
Optionally, the acquiring the user behavior data specifically includes: receiving a user behavior data packet sent by terminal equipment; and preprocessing the user behavior data packet to obtain the user behavior data.
By adopting the technical scheme, the intelligent door lock can receive the user behavior data packet sent by the terminal equipment and preprocess the user behavior data packet, thereby improving the quality and accuracy of data and laying a foundation for subsequent analysis and application.
Optionally, the inputting the user behavior data into a preset recognition model to obtain an output result specifically includes: determining a first behavior feature according to the user behavior data; searching the first behavior characteristic in the preset recognition model; if a second behavior feature corresponding to the first behavior feature exists in the preset recognition model, a first output result is obtained, wherein the first output result is used for indicating that the first behavior feature is abnormal, the first behavior feature and the second behavior feature are the same behavior feature, and a plurality of abnormal behavior features are stored in the preset recognition model.
By adopting the technical scheme, the intelligent door lock can detect abnormal behavior characteristics by matching the user behavior data with the preset identification model. Compared with manual analysis or traditional rule judgment, the automatic process can greatly improve the recognition speed and accuracy and timely respond to the potential abnormal behaviors, so that more accurate abnormal behavior detection and warning are provided, the protection capability of the door lock is enhanced, and the property and personal safety of a user are ensured.
Optionally, the determining an alarm mode according to the facial feature data of the user specifically includes: searching the facial feature data of the user in a preset facial feature database; if the facial feature data of the user does not exist in the preset facial feature database, a first alarm mode is obtained, and the first alarm mode is used for warning the user of being far away; and if the facial feature data of the user exist in the preset facial feature database, a second alarm mode is obtained, and the second alarm mode is used for prompting the user to wait.
By adopting the technical scheme, the intelligent door lock searches the facial feature data of the user in the preset facial feature database, so that only authorized users can use the door lock, and the safety of an access control system is improved. When the facial feature data of the user does not exist in the preset facial feature database, a first alarm mode is adopted to warn the user to keep away so as to avoid the entry of unauthorized people. When the facial feature data of the user exist, the second alarm mode is adopted to prompt the user to wait, the probability of unstable emotion of the user caused by the fact that the facial recognition needs to be further verified is reduced, and the intelligent door lock provides convenient user experience while guaranteeing safety.
Optionally, after the determining the alarm mode, the method further includes: monitoring the user behavior data and determining the user behavior time length; if the user behavior time length is greater than or equal to a preset time length threshold, generating an emergency alarm instruction, wherein the emergency alarm instruction comprises specific alarm position information; and sending the emergency alarm instruction to an emergency department so that the emergency department can take corresponding measures when the emergency department goes to specific alarm position information.
By adopting the technical scheme, the intelligent door lock can track and record the activities of the user in the access control area in real time through monitoring the user behavior data. This helps to discover unusual behavior, threats, and dangerous situations, improving security of the access control system. And when the time length of the user action is greater than or equal to a preset time length threshold value, the intelligent door lock generates an emergency alarm instruction. Thus, the emergency situation possibly existing can be responded quickly, corresponding measures can be taken in time, and the property and personal safety of the user are protected. Finally, the intelligent door lock provides the abnormal behavior position for the emergency department, so that the emergency department can accurately go to the alarm position to take corresponding measures. This helps to shorten the time of emergency response, and to increase the efficiency of handling emergency situations, thereby further ensuring the safety and benefits of the user.
Optionally, training the preset recognition model before the user behavior data is input into the preset recognition model to obtain an output result; the training of the preset recognition model specifically comprises the following steps: acquiring training information, wherein the training information comprises user behavior data and an output result; inputting the training information into a self-adaptive feature fusion network for training to obtain a first training result; superposing and standardizing the first training result and the training information to obtain a second training result; inputting the second training result into the self-adaptive feature fusion network for processing to obtain a third training result; and superposing and standardizing the third training result and the second training result until the training information similarity matrix is output, wherein the training information similarity matrix meets a preset logistic regression condition.
By adopting the technical scheme, the intelligent door lock is trained by using the user behavior data and the output result, so that the preset recognition model can learn more accurate and more reliable modes and features. Through repeated iterative superposition and standardization processing, the training result of the model can be further optimized, and the accuracy and stability of the model can be improved. The self-adaptive feature fusion network is used for training the preset recognition model, and the weight and the combination mode of the features can be dynamically adjusted. Therefore, the model can be better adapted to different types of user behavior data, and the model is effectively fused with the output result. The generalization capability and the adaptability of the model can be improved through self-adaptive fusion. Through repeated iterative superposition and standardization processing, the training information similarity matrix continuously approaches the preset logistic regression condition. The iterative training process can help the model to be continuously optimized, and further improves the performance and the precision of the model.
Optionally, acquiring help-seeking voice information of the user; and sending help seeking information to a preset emergency contact person according to the help seeking voice information.
By adopting the technical scheme, the intelligent door lock can timely capture the emergency situation encountered by the user by acquiring the help calling voice information of the user, so that actions can be immediately taken and the help calling information can be sent to the emergency contact person, and the intelligent door lock can be helped and supported as soon as possible. The help seeking information is automatically sent, so that the help seeking process of the user can be simplified, the door lock operation burden of the user under emergency conditions is reduced, and the help seeking efficiency and convenience are improved.
In a second aspect of the present application, an intelligent door lock alarm control device is provided, where the alarm control device is an intelligent door lock, and the intelligent door lock includes an acquisition module and a processing module, where the acquisition module is configured to acquire user behavior data; the processing module is used for inputting the user behavior data into a preset recognition model to obtain an output result; the acquisition module is further used for acquiring facial feature data of the user if the output result is abnormal; and the processing module is also used for determining an alarm mode according to the facial feature data so as to execute alarm operation according to the alarm mode.
In a third aspect of the present application, there is provided an electronic device comprising a processor, a memory for storing instructions, a user interface and a network interface, both for communicating to other devices, the processor being adapted to execute the instructions stored in the memory to cause the electronic device to perform the method as described above.
In a fourth aspect of the present application, there is provided a computer readable storage medium storing instructions that, when executed, perform a method as described above.
In summary, one or more technical solutions provided in the embodiments of the present application at least have the following technical effects or advantages:
1. the intelligent door lock firstly acquires user behavior data, and then inputs the user behavior data into a preset recognition model, so that an output result is obtained. When the intelligent door lock determines that the output result is abnormal, facial feature data of the user can be acquired. Next, the intelligent door lock determines an alarm mode according to the facial feature data of the user, so that an alarm is timely carried out according to the alarm mode;
2. the intelligent door lock can detect abnormal behavior characteristics by matching the user behavior data with a preset identification model. Compared with the related art, the automatic process can greatly improve the recognition speed and accuracy and timely respond to the potential abnormal behaviors, so that more accurate abnormal behavior detection and warning are provided, the protection capability of the door lock is enhanced, and the property and personal safety of a user are ensured;
3. when the facial feature data of the user does not exist in the preset facial feature database, a first alarm mode is adopted to warn the user to keep away so as to avoid the entry of unauthorized people. When the facial feature data of the user exist, the second alarm mode is adopted to prompt the user to wait, the probability of unstable emotion of the user caused by the fact that the facial recognition needs to be further verified is reduced, and the intelligent door lock provides convenient user experience while guaranteeing safety.
Drawings
Fig. 1 is a schematic flow chart of an intelligent door lock alarm control method according to an embodiment of the present application.
Fig. 2 is a schematic block diagram of an intelligent door lock alarm control device according to an embodiment of the present application.
Fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Reference numerals illustrate: 21. an acquisition module; 22. a processing module; 31. a processor; 32. a communication bus; 33. a user interface; 34. a network interface; 35. a memory.
Detailed Description
In order to make those skilled in the art better understand the technical solutions in the present specification, the technical solutions in the embodiments of the present specification will be clearly and completely described below with reference to the drawings in the embodiments of the present specification, and it is obvious that the described embodiments are only some embodiments of the present application, but not all embodiments.
In the description of embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described herein as "such as" or "for example" should not be construed as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "or" for example "is intended to present related concepts in a concrete fashion.
In the description of the embodiments of the present application, the term "plurality" means two or more. For example, a plurality of systems means two or more systems, and a plurality of screen terminals means two or more screen terminals. Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating an indicated technical feature. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless expressly specified otherwise.
The intelligent door lock is an advanced lockset for improving the self-traditional mechanical lock, and has the characteristics of more intellectualization and convenience in the aspects of user safety, identification and management.
Currently, an intelligent door lock is unlocked in a digital password mode, a fingerprint mode or a face identification mode and the like. The unlocking modes are simple and convenient to operate, high in unlocking efficiency and convenient for users to use. However, when personnel unrelated to the smart door lock user attempt to perform an unlocking action, they often take extreme means to break the smart door lock for theft purposes. It is worth noting that the current intelligent door lock technology cannot intelligently identify such extreme means, and therefore cannot alarm in time.
Therefore, in order to improve the safety and protection capability of the intelligent door lock, innovative technologies are further developed to cope with the unlocking behavior of personnel unrelated to the user of the intelligent door lock.
In order to solve the above technical problems, the present application provides an intelligent door lock alarm control method, and referring to fig. 1, fig. 1 is a schematic flow chart of an intelligent door lock alarm control method provided in an embodiment of the present application. The alarm control method is applied to the intelligent door lock and comprises the following steps of S110 to S140:
s110, acquiring user behavior data.
Specifically, the intelligent door lock is used for realizing real-time monitoring of the user behavior by acquiring the user behavior data of the door control system in real time. The intelligent door lock can be an intelligent door lock used by a household resident and also can be an intelligent door lock used by a community access control. The user behavior data refers to behavior operations of the user with respect to the intelligent door lock, and for example, the user behavior data includes password input behavior to the intelligent door lock, violent disassembly destructive behavior to the intelligent door lock, and pulling collision behavior to the door. At this time, the user is a person who wants to enter the home resident through the intelligent door lock, or a person who enters the community entrance guard through the intelligent door lock.
In one possible implementation manner, the method for acquiring the user behavior data specifically includes: receiving a user behavior data packet sent by terminal equipment; and preprocessing the user behavior data packet to obtain user behavior data.
Specifically, the intelligent door lock firstly receives a user behavior data packet sent by the terminal equipment, and then preprocesses the user behavior data packet, so as to obtain user behavior data. The terminal equipment refers to sensor equipment which is linked with the intelligent door lock. For example, the terminal device comprises a pressure sensor and/or a vibration sensor arranged on the handle of the intelligent door lock, a noise sensor and/or a pressure sensor on the door plate where the intelligent door lock is arranged, and the like, or a user behavior image shot by the image acquisition device. In the embodiment of the present application, the user behavior data packet may be further understood as a combination of sensor data obtained by the terminal device after the user operates the intelligent door lock. The preprocessing mode comprises the steps of integrating and cleaning sensor data measured by the terminal equipment, so that high-quality data which can be identified by the model are screened out, and the subsequent processing of the model is facilitated.
S120, inputting the user behavior data into a preset recognition model to obtain an output result.
Specifically, after the intelligent door lock acquires the user behavior data, the user behavior data is input into a preset recognition model, so that an output result corresponding to the preset recognition model is obtained. For example, the user behavior data includes pressure value, noise and vibration, the preset recognition model is compared with the normal user door opening behavior data, when the pressure sensor data shows that the door lock pressure exceeds the normal door opening pressure value and is accompanied by strong vibration and huge noise, the user can be determined to be illegally intruded, at this time, the preset recognition model generates an output result, and the output result is used for indicating that the user behavior data is abnormal, and that illegal intrusion or violent door opening behaviors possibly exist.
In one possible implementation manner, the user behavior data is input into a preset recognition model to obtain an output result, which specifically includes: determining a first behavior feature according to the user behavior data; searching a first behavior feature in a preset recognition model; if a second behavior feature corresponding to the first behavior feature exists in the preset recognition model, a first output result is obtained, wherein the first output result is used for representing that the first behavior feature is abnormal, the first behavior feature and the second behavior feature are the same behavior feature, and a plurality of abnormal behavior features are stored in the preset recognition model.
Specifically, the intelligent door lock determines a first behavior feature according to user behavior data, searches a preset recognition model for the first behavior feature, and generates a first output result when a second behavior feature corresponding to the first behavior feature exists in the preset recognition model. The first behavior feature comprises a plurality of behavior features analyzed according to user behavior data, for example, the first behavior feature comprises a limb impact door plate, a powerful intelligent door lock or a door handle. The first behavior feature and the second behavior feature are the same behavior feature, and in this embodiment, it may be understood that the similarity between the first behavior feature and the second behavior feature reaches a certain threshold, and the calculation modes of the similarity between the first behavior feature and the second behavior feature include, but are not limited to, calculation modes of euler angle similarity, cosine similarity, hamming similarity, and the like. The first output is used to indicate that the first behavioral characteristic, i.e., the user behavioral data, is abnormal, there may be potential burglary or other extreme behavior.
S130, if the output result is abnormal, acquiring facial feature data of the user.
Specifically, when the intelligent door lock determines that the output result is abnormal, the facial feature data of the user is actively acquired. The facial feature data of the user refer to a plurality of facial features of the user when the intelligent door lock is operated, for example, the feature positions may be any one of the mouth, nose, eyebrow, left eye, right eye and tongue, and may also be sweat and facial color, and specific types are set according to specific situations, which are not repeated here. The facial feature data of the user can be acquired through an image acquisition device, wherein the image acquisition device can be a 3DTOF depth camera or an RGB imaging camera, and the specific type is set according to specific conditions and is not repeated here. According to the embodiment of the application, the 3DTOF depth camera and the RGB imaging camera are preferably matched, so that a shot image is clearer and more accurate, and the intelligent door lock is convenient to acquire more accurate image information. The intelligent door lock can communicate with the camera through a wired or wireless network.
And S140, determining an alarm mode according to the facial feature data of the user so as to execute alarm operation according to the alarm mode.
Specifically, the intelligent door lock determines an alarm mode according to facial feature data of a user, so that the intelligent door lock can conveniently carry out alarm operation according to the alarm mode. The alarm mode can be an alarm mode preset by the intelligent door lock, and can be determined by real-time intelligent analysis of the intelligent door lock according to the facial feature data. For example, the alarm mode may be a flash lamp preset for the intelligent door lock to burst and a horn to broadcast warning voice, etc. Therefore, the intelligent door lock can alarm in time according to an alarm mode.
In one possible implementation manner, the alarm mode is determined according to the facial feature data of the user, and specifically includes: searching facial feature data in a preset facial feature database; if the facial feature data of the user does not exist in the preset facial feature database, a first alarm mode is obtained, and the first alarm mode is used for warning the user of being far away; if the facial feature data of the user exist in the preset facial feature database, a second alarm mode is obtained, and the second alarm mode is used for prompting the user to wait.
Specifically, the above process is a specific process of determining an alarm mode by using the intelligent door lock provided in the embodiment of the present application. Firstly, the intelligent door lock searches the facial feature data of the user in a preset facial feature database, when the facial feature data of the user does not exist in the preset facial feature database, the intelligent door lock indicates that the user has potential risks, and a first alarm mode is generated by the intelligent door lock and is used for warning or warning the user to be far away from the intelligent door lock. When the facial feature data of the user exist in the preset facial feature database, a second alarm mode is obtained and used for prompting the user to stand by, so that the intelligent door lock is prevented from delaying, the emotion of the user is benefited, and the use experience of the user is improved.
The preset facial database is a database pre-established by the user, for example, when the intelligent door lock is a home resident door lock, the user may include all members in the home, and facial feature data of all home members are stored in the preset facial feature database. The facial feature data in the preset facial feature database supports additions or deletions, which are not described in detail herein.
In one possible implementation manner, after determining the alarm mode, the method further includes: monitoring the user behavior data and determining the user behavior time length; if the time length of the user behavior is greater than or equal to a preset time length threshold value, generating an emergency alarm instruction, wherein the emergency alarm instruction comprises specific alarm position information; and sending an emergency alarm instruction to an emergency department so that the emergency department can take corresponding measures when the emergency department goes to specific alarm position information.
Specifically, after the intelligent door lock determines the alarm mode, the user behavior data is monitored, so that the user behavior duration is determined. When the user behavior time is greater than or equal to the preset time threshold, the intelligent door lock generates an emergency alarm instruction. Finally, the intelligent door lock sends the emergency alarm instruction to the emergency department, so that the emergency department can conveniently and timely go to the emergency department according to the specific alarm position information in the emergency alarm instruction, the potential risk is relieved, and the personal safety and the property safety are greatly protected. The user behavior duration may be understood as a duration that a user continuously uses the intelligent door lock, or a duration that the user continuously collides with the door lock or colludes to damage the door panel in the embodiment of the present application. The preset time period threshold may be set according to practical situations, such as 3 minutes, 5 minutes, 10 minutes, and the like. The emergency department can comprise a property management department corresponding to the district, a public security department and the like. The contact mode of the specific emergency department can be preset by the owner of the intelligent door lock, so that the time of emergency response is shortened, the emergency situation processing efficiency is improved, and the safety and benefits of users are further ensured.
In one possible implementation, the preset recognition model is trained before the user behavior data is input into the preset recognition model to obtain an output result; training a preset recognition model, which specifically comprises the following steps: acquiring training information, wherein the training information comprises user behavior data and an output result; inputting training information into a self-adaptive feature fusion network for training to obtain a first training result; the first training result and the training information are overlapped and standardized to obtain a second training result; inputting the second training result into the self-adaptive feature fusion network for processing to obtain a third training result; and superposing and standardizing the third training result and the second training result until a training information similarity matrix is output, wherein the training information similarity matrix meets the preset logistic regression condition.
Specifically, before the preset recognition model is applied to the intelligent door lock, the preset recognition model is trained, and the process is a process of training the preset recognition model. The adaptive feature fusion network is a neural network structure for training a model, and aims to integrate and fuse features of different sources or types so as to extract richer and more accurate feature representations. Adaptive feature fusion networks are typically composed of multiple layers, each with different functions and operations. The following is a detailed explanation of the adaptive feature fusion network in the embodiments of the present application. The self-adaptive feature fusion network comprises an input layer, a feature extraction layer, a feature fusion layer, a self-adaptive feature layer and an output layer.
Further, the input layer receives feature data from the training information, and the input includes user behavior data and output results. The feature extraction layer processes the input features and may use a convolution layer, a pooling layer, or a fully connected layer to extract advanced feature representations. The goal of the feature fusion layer is to integrate and fuse features of different sources or types. This may be achieved by superimposing, stitching or weighted summing the features to produce a more comprehensive representation of the features. The adaptive feature layer is a core part of the adaptive feature fusion network. It adopts different mechanisms to dynamically adjust the weight or importance of the features to accommodate different characteristics of the current task or data. This may be achieved by an attention-introducing mechanism, a gating unit or other learnable mechanism. The output layer receives the final feature representation from the self-adaptive feature layer and generates a corresponding output result so as to complete training of the preset recognition model.
In general, the self-adaptive feature fusion network can utilize features of different types and sources to generate more abundant and accurate feature representations through feature extraction, integration and adjustment processes, so that the recognition performance and generalization capability of a preset recognition model are improved.
In one possible implementation, help-seeking voice information of a user is acquired; and sending help seeking information to a preset emergency contact person according to the help seeking voice information.
Specifically, when a person unrelated to the intelligent door lock user approaches the intelligent door lock, the delusions picture enters the room in an extreme mode, the intelligent door lock can acquire help-seeking voice information of the user in real time, and therefore the user is helped to give an alarm. Next, the intelligent door lock will send help information to emergency contacts pre-stored in the intelligent door lock based on the help voice information. The method for acquiring the help-seeking voice information comprises the steps of acquiring voice information of a help seeker in a linkage mode through a sound acquisition device, and processing the voice information, wherein the voice information comprises voice conversion text, alarm place and time adding and image data of a person, which is faced by a user and is irrelevant to the intelligent door lock user, for operating the intelligent door lock. The preset emergency contacts can be set as family members and law enforcement personnel in the police department, or staff in the emergency department of the hospital, etc.
The application also provides an intelligent door lock alarm control device, referring to fig. 2, fig. 2 is a schematic block diagram of the intelligent door lock alarm control device provided in the embodiment of the application. The alarm control device is an intelligent door lock, and the intelligent door lock comprises an acquisition module 21 and a processing module 22, wherein the acquisition module 21 is used for acquiring user behavior data; the processing module 22 is configured to input user behavior data into a preset recognition model to obtain an output result; the obtaining module 21 is further configured to obtain facial feature data of the user if it is determined that the output result is abnormal; the processing module 22 is further configured to determine an alarm mode according to the facial feature data of the user, so as to perform an alarm operation according to the alarm mode.
In one possible implementation, the obtaining module 21 obtains user behavior data, specifically includes: the acquisition module 21 receives a user behavior data packet sent by the terminal equipment; the processing module 22 pre-processes the user behavior data packet to obtain user behavior data.
In one possible implementation, the processing module 22 inputs the user behavior data into a preset recognition model to obtain an output result, and specifically includes: the processing module 22 determines a first behavioral characteristic from the user behavioral data; the processing module 22 searches the preset recognition model for the first behavior feature; if there is a second behavior feature corresponding to the first behavior feature in the preset recognition model, the processing module 22 obtains a first output result, where the first output result is used to indicate that the first behavior feature is abnormal, the first behavior feature and the second behavior feature are the same behavior feature, and multiple abnormal behavior features are stored in the preset recognition model.
In one possible implementation, the processing module 22 determines an alarm mode according to the facial feature data of the user, specifically including: the processing module 22 searches the preset facial feature database for facial feature data of the user; if the facial feature data of the user does not exist in the preset facial feature database, the processing module 22 obtains a first alarm mode, and the first alarm mode is used for warning the user of the distance; if the facial feature data of the user exists in the preset facial feature database, the processing module 22 obtains a second alarm mode, and the second alarm mode is used for prompting the user to wait.
In one possible implementation, after the processing module 22 determines the alarm mode, the method further includes: the processing module 22 monitors the user behavior data and determines the duration of the user behavior; if the user behavior time period is greater than or equal to the preset time period threshold, the processing module 22 generates an emergency alert instruction, wherein the emergency alert instruction comprises specific alert location information; the processing module 22 sends an emergency alert instruction to the emergency department to cause the emergency department to take corresponding action to the specific alert location information.
In one possible implementation, the preset recognition model is trained before the processing module 22 inputs the user behavior data into the preset recognition model to obtain the output result; the processing module 22 trains a preset recognition model, specifically including: the acquisition module 21 acquires training information including user behavior data and an output result; the processing module 22 inputs the training information into the self-adaptive feature fusion network for training to obtain a first training result; the processing module 22 performs superposition and standardization processing on the first training result and the training information to obtain a second training result; the processing module 22 inputs the second training result into the adaptive feature fusion network to be processed, so as to obtain a third training result; the processing module 22 performs superposition and standardization processing on the third training result and the second training result until a training information similarity matrix is output, where the training information similarity matrix meets a preset logistic regression condition.
In one possible implementation, the obtaining module 21 obtains help-seeking voice information of the user; the processing module 22 sends help seeking information to a preset emergency contact according to the help seeking voice information.
The application further provides an electronic device, and referring to fig. 3, fig. 3 is a schematic structural diagram of the electronic device provided in the embodiment of the application. The electronic device may include: at least one processor 31, at least one network interface 34, a user interface 33, a memory 35, at least one communication bus 32.
Wherein the communication bus 32 is used to enable connected communication between these components.
The user interface 33 may include a Display screen (Display) and a Camera (Camera), and the optional user interface 33 may further include a standard wired interface and a standard wireless interface.
The network interface 34 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), among others.
Wherein the processor 31 may comprise one or more processing cores. The processor 31 connects various parts within the overall server using various interfaces and lines, performs various functions of the server and processes data by executing or executing instructions, programs, code sets, or instruction sets stored in the memory 35, and invoking data stored in the memory 35. Alternatively, the processor 31 may be implemented in hardware in at least one of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), programmable logic array (Programmable Logic Array, PLA). The processor 31 may integrate one or a combination of several of a central processing unit (Central Processing Unit, CPU), an image processor (Graphics Processing Unit, GPU), and a modem etc. The CPU mainly processes an operating system, a user interface, an application program and the like; the GPU is used for rendering and drawing the content required to be displayed by the display screen; the modem is used to handle wireless communications. It will be appreciated that the modem may not be integrated into the processor 31 and may be implemented by a single chip.
The Memory 35 may include a random access Memory (Random Access Memory, RAM) or a Read-Only Memory (Read-Only Memory). Optionally, the memory 35 includes a non-transitory computer readable medium (non-transitory computer-readable storage medium). Memory 35 may be used to store instructions, programs, code sets, or instruction sets. The memory 35 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the above-described respective method embodiments, etc.; the storage data area may store data or the like involved in the above respective method embodiments. The memory 35 may alternatively be at least one memory device located remotely from the aforementioned processor 31. As shown in fig. 3, an operating system, a network communication module, a user interface module, and an application program of an intelligent door lock alarm control method may be included in the memory 35 as a computer storage medium.
In the electronic device shown in fig. 3, the user interface 33 is mainly used for providing an input interface for a user, and acquiring data input by the user; and processor 31 may be configured to invoke an application program in memory 35 that stores an intelligent door lock alarm control method that, when executed by one or more processors, causes the electronic device to perform the method as in one or more of the embodiments described above.
It should be noted that, for simplicity of description, the foregoing method embodiments are all expressed as a series of action combinations, but it should be understood by those skilled in the art that the present application is not limited by the order of actions described, as some steps may be performed in other order or simultaneously in accordance with the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required in the present application.
The present application also provides a computer-readable storage medium having instructions stored thereon. When executed by one or more processors, cause an electronic device to perform the method as described in one or more of the embodiments above.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments.
In the several embodiments provided herein, it should be understood that the disclosed apparatus may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, such as a division of units, merely a division of logic functions, and there may be additional divisions in actual implementation, such as multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some service interface, device or unit indirect coupling or communication connection, electrical or otherwise.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable memory. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a memory, including several instructions for causing a computer device (which may be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the methods of the embodiments of the present application. And the aforementioned memory includes: various media capable of storing program codes, such as a U disk, a mobile hard disk, a magnetic disk or an optical disk.
The foregoing is merely exemplary embodiments of the present disclosure and is not intended to limit the scope of the present disclosure. That is, equivalent changes and modifications are contemplated by the teachings of this disclosure, which fall within the scope of the present disclosure. Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a scope and spirit of the disclosure being indicated by the claims.

Claims (10)

1. An intelligent door lock alarm control method, which is characterized by being applied to an intelligent door lock, comprising:
acquiring user behavior data;
inputting the user behavior data into a preset recognition model to obtain an output result;
if the output result is abnormal, acquiring facial feature data of the user;
and determining an alarm mode according to the facial feature data of the user so as to execute alarm operation according to the alarm mode.
2. The alarm control method according to claim 1, wherein the obtaining user behavior data specifically includes:
receiving a user behavior data packet sent by terminal equipment;
and preprocessing the user behavior data packet to obtain the user behavior data.
3. The alarm control method according to claim 1, wherein the step of inputting the user behavior data into a preset recognition model to obtain an output result specifically includes:
determining a first behavior feature according to the user behavior data;
searching the first behavior characteristic in the preset recognition model;
if a second behavior feature corresponding to the first behavior feature exists in the preset recognition model, a first output result is obtained, wherein the first output result is used for indicating that the first behavior feature is abnormal, the first behavior feature and the second behavior feature are the same behavior feature, and a plurality of abnormal behavior features are stored in the preset recognition model.
4. The alarm control method according to claim 1, wherein the determining an alarm mode according to the facial feature data of the user specifically includes:
searching the facial feature data of the user in a preset facial feature database;
if the facial feature data of the user does not exist in the preset facial feature database, a first alarm mode is obtained, and the first alarm mode is used for warning the user of being far away;
and if the facial feature data of the user exist in the preset facial feature database, a second alarm mode is obtained, and the second alarm mode is used for prompting the user to wait.
5. The alarm control method according to claim 4, characterized in that after said determining an alarm mode, the method further comprises:
monitoring the user behavior data and determining the user behavior time length;
if the user behavior time length is greater than or equal to a preset time length threshold, generating an emergency alarm instruction, wherein the emergency alarm instruction comprises specific alarm position information;
and sending the emergency alarm instruction to an emergency department so that the emergency department can take corresponding measures when the emergency department goes to specific alarm position information.
6. The alarm control method according to claim 1, wherein the preset recognition model is trained before the user behavior data is input into the preset recognition model to obtain an output result; the training of the preset recognition model specifically comprises the following steps:
acquiring training information, wherein the training information comprises user behavior data and an output result;
inputting the training information into a self-adaptive feature fusion network for training to obtain a first training result;
superposing and standardizing the first training result and the training information to obtain a second training result;
inputting the second training result into the self-adaptive feature fusion network for processing to obtain a third training result;
and superposing and standardizing the third training result and the second training result until the training information similarity matrix is output, wherein the training information similarity matrix meets a preset logistic regression condition.
7. The alarm control method according to claim 1, characterized in that the method further comprises:
acquiring help-seeking voice information of a user;
and sending help seeking information to a preset emergency contact person according to the help seeking voice information.
8. An intelligent door lock alarm control device is characterized in that the alarm control device is an intelligent door lock, the intelligent door lock comprises an acquisition module (21) and a processing module (22), wherein,
the acquisition module (21) is used for acquiring user behavior data;
the processing module (22) is used for inputting the user behavior data into a preset recognition model to obtain an output result;
the acquisition module (21) is further used for acquiring facial feature data of a user if the output result is abnormal;
the processing module (22) is further used for determining an alarm mode according to the facial feature data of the user so as to execute alarm operation according to the alarm mode.
9. An electronic device, characterized in that the electronic device comprises a processor (31), a memory (35), a user interface (33) and a network interface (34), the memory (35) being adapted to store instructions, the user interface (33) and the network interface (34) being adapted to communicate to other devices, the processor (31) being adapted to execute the instructions stored in the memory (35) to cause the electronic device to perform the method according to any one of claims 1 to 7.
10. A computer readable storage medium storing instructions which, when executed, perform the method of any one of claims 1 to 7.
CN202311103650.4A 2023-08-30 2023-08-30 Intelligent door lock alarm control method and device and electronic equipment Pending CN117315818A (en)

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Application Number Priority Date Filing Date Title
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Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202311103650.4A CN117315818A (en) 2023-08-30 2023-08-30 Intelligent door lock alarm control method and device and electronic equipment

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Publication Number Publication Date
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Country Link
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