312 results about "Home security" patented technology

The invention discloses a household Internet of Things integrated fire alarm system and a linkage control method thereof, and relates to the technical field of smart home security. A first sensing recognition module, a second deep recognition module, a linkage control module, a cloud management platform module and a system self-checking module are integrated; the problems that in an existing fire alarm system, the false alarm rate is high, linkage equipment is prone to false triggering, linkage control logic is complex, and errors are prone to occurring are effectively solved. According to the invention, multi-modal data fusion and a deep learning algorithm are adopted to carry out fire identification, a dynamic threshold adjustment algorithm is combined to optimize the sensitivity of a sensor, and intelligent linkage configuration and an anomaly detection function are provided through a cloud management platform; the accuracy of fire identification and the stability of the system are greatly improved, the credibility of a user to the system and the comprehensiveness of family safety protection are remarkably improved, and resource waste and unnecessary panic caused by false alarm are effectively reduced.

The invention relates to the technical field of security alarm, in particular to a home security integrated system and method based on artificial intelligence, and the system comprises a multi-source data collection module which collects environment data through an infrared sensor, a thermal imaging camera and a standard video camera, carries out the data fusion, and generates an environment data fusion result. According to the invention, the acquisition module fuses infrared, thermal imaging and video cameras, so that the monitoring coverage is expanded, the capability of capturing environmental details is enhanced, and the detection rate of abnormal behaviors is improved. Standardized monitoring data is generated through data fusion and format unified processing, high-quality input is provided for feature extraction and behavior analysis, data streams are optimized, and information loss is reduced. A pre-trained deep learning model is applied to an intelligent analysis module, recognition of complex behavior modes is enhanced, and the accuracy and timeliness of anomaly detection are improved.

The invention discloses a home monitoring method and system based on a multi-agent large model, the multi-agent large model comprises a central large model and agents, and the central large model is used for scheduling at least one agent to execute monitoring, analysis and alarm tasks; the method comprises the following steps: reading video stream data of a household camera in real time, and preprocessing source data to obtain unified video format data; and inputting the processed video data into the multi-agent large model, distributing a task to at least one agent by the central large model, and executing corresponding monitoring, analysis and alarm operations by the at least one agent according to the task. By performing uniform format processing on real-time read camera video streams, the accuracy of system data is ensured, and monitoring identification and intelligent alarm of abnormal conditions in a home environment are realized through a multi-agent collaborative decision-making mechanism in combination with intelligent analysis and reasoning capabilities of a large language model. And the reliability, the intelligence, the user experience and the like of the home safety monitoring system are remarkably improved.

The present disclosure provides a comprehensive system for remote property monitoring utilizing mobile devices, autonomous drones, and artificial intelligence (AI). The system includes a mobile application for user registration and monitoring requests, a central processing server for data analysis and management, and drones and mobile devices for capturing high-resolution images and videos. AI algorithms process the captured data to detect anomalies or unauthorized activities. The system generates real-time alerts and detailed reports, which are sent to property owners via the mobile application. The flexibility of the system allows it to be hosted in the cloud, on-premises, or in a hybrid model. The invention also supports integration with third-party home security systems, enhancing overall security. Key applications include property security, agricultural monitoring, infrastructure inspection, environmental conservation, disaster response, and urban planning. This automated, scalable, and efficient system provides robust, real-time property surveillance and management.

The invention relates to the technical field of smart home security and protection, and discloses a smart home security and protection system based on a body, and the method comprises the steps: collecting real-time sensing data through Wi-Fi multipath scattered waves, constructing a body sensing network, recognizing behavior feature information, optimizing a preset security and protection model decision architecture, and obtaining an optimized security and protection process. The method comprises the following steps: firstly, generating a security response path, determining a personnel activity space-time distribution state, calculating a security efficiency value, then determining an optimization direction according to the security efficiency value, generating a dynamic protection organization unit, calculating a misjudgment attenuation rate, and finally determining a security state and analyzing a security dimension according to the misjudgment attenuation rate, thereby constructing a more accurate and intimate intelligent security scheme. According to the invention, the intelligent level of autonomous decision making and active protection of home security can be improved.

The invention discloses a door lock collaborative identity recognition method and system, and the method comprises the steps: obtaining a locally stored MAC-face binding relation table according to face recognition verification completed by a resident through an intelligent door lock; according to the moving path of the resident from the floor entrance to the home door lock, identity recognition is completed through cooperation of multicast communication and the home door lock, an identity confirmation result is obtained, and a directional multicast network is constructed; according to a leaving event that a resident opens a door lock from a room, triggering a passing door lock to collect leaving person information, and obtaining a leaving person identity confirmation result; and selecting to send a unicast or multicast confirmation message by the own door lock according to the completion degree of identity confirmation of the leaving person, and triggering the passing door lock to send an IGMP leaving message to the router to obtain a simplified multicast forwarding table. According to the embodiment of the invention, seamless identity cooperation of the whole process from home returning to home leaving can be realized, and the automation degree of home security and protection and the user experience are improved.

The invention discloses a non-intrusive dual-channel smart home security monitoring method, and belongs to the technical field of network security. Aiming at the problem that the existing smart home privacy security monitoring method has defects, an audio channel takes a pre-training model Vgish as an audio feature extractor, and a pixel spectrogram corresponding to each section of 960ms frame length audio obtained through discrete Fourier framing processing is vectorized into 128-dimensional feature representation; then, a support vector machine (SVM-RBF) classifier identifies and obtains an audio event category sequence Ea; five-tuple information contained in event traffic is extracted in a traffic channel to be matched with a predefined event-rule base mode, a traffic event category sequence Et is recognized, and then event alignment, verification and fusion are carried out on the sequences Es and Et so as to monitor the security problem in the smart home. According to the method, the accuracy of event identification of the two-channel model is proved through an experiment carried out on a self-built experiment platform, and meanwhile, the necessity of safety monitoring by adopting two-channel fusion is also proved.

Determining a sequence for providing a notification regarding activity recorded by a camera is described. In one aspect, a priority sequence for can be determined based on a variety of characteristics of the available devices registered with the home security system of the camera.

The invention relates to the field of home safety, in particular to an AI intelligent home safety control system. Comprising a management center, and the management center is in communication connection with a home user behavior module, a home user control module and a safety detection control module. Acquiring historical instruction data of a user for the smart home, analyzing the historical instruction data, constructing a user instruction map, and generating user control data in combination with the user instruction map; constructing a network path link in combination with the historical instruction data and the user control data; analyzing the network path chain to generate a standard path chain template; generating a real-time home path chain through the obtained current control behavior of the user, and obtaining a home behavior control model according to the user instruction map and the network path chain; and analyzing the collected real-time instruction data in combination with the home behavior control model, generating a risk mapping index table, and performing safety control on the smart home through the risk mapping index table. According to the invention, the security of the smart home can be improved.

The invention relates to the technical field of smart home security, and discloses a smart AI-based home security real-time video anti-shielding defense system, which comprises a monitoring unit, a smart network camera unit, a signal shielding detection unit, a terminal control unit and an execution unit, the monitoring unit is used for detecting the surrounding environment of the detection unit in different dimensions or controlling the monitoring unit to change the environment through a terminal, and the intelligent network camera unit serves as a core unit of the system and is used for receiving data information collected by the monitoring unit and pushing the data information to the terminal control unit in an APP mode. The terminal control unit is used for receiving a control command sent by the intelligent network camera unit and displaying data of the intelligent network camera unit, the signal shielding detection unit is used for carrying out electromagnetic safety detection on the intelligent network camera unit in a family space environment, and an alarm is given if abnormity comprises malicious radio attack. And the execution unit is used for receiving instruction information sent by the terminal control unit through the intelligent network camera unit, sending data information to access the execution unit after accurate confirmation of audio and video, and executing security and fire-fighting tasks according to the instruction.

The invention discloses a home security prediction method and device, electronic equipment and a storage medium, and relates to the technical field of data processing. Visual and non-visual data can be fused to generate perceptual event features, and a dynamic causal knowledge graph including home entities, events and situational states is constructed; deep modeling of association between the overall state of the family and the event causality is realized; complex scene anomalies can be captured through causal chain template matching and anomaly marking, multiple groups of causal hypotheses are generated, an optimal result is screened, explainable early warning containing causal logic is generated in combination with risk scores, and the limitation of a traditional static rule or a single sensor is avoided, so that the method is suitable for popularization and application. The technical problem that an existing traditional scheme lacks deep modeling capability of family overall state, member behavior mode and causal association between events and is difficult to adapt to complex dynamic scenes can be solved. The technical effects of improving the adaptability of the home security system to complex dynamic scenes, improving the early warning accuracy and interpretability and enhancing the intelligent level of the system are achieved.

The invention relates to the technical field of smart home, and discloses a real-time video behavior recognition and anomaly monitoring system based on edge computing, which comprises an edge computing node, a video preprocessing module, a background separation and target detection module, an alarm triggering and equipment linkage module, a behavior recognition module and an anomaly monitoring module, the edge computing node is connected with the video preprocessing module and the alarm triggering and equipment linkage module, and the edge computing node is connected with the video preprocessing module and the alarm triggering and equipment linkage module. The system has multiple advantages of real-time performance and high efficiency, privacy protection and security, accurate identification and low false alarm rate, equipment linkage and intelligence, strong adaptability and expandability, energy conservation and environmental protection, and the like, and the system has wide application prospects and important practical values in the field of smart home security protection due to the advantages.

The invention discloses a cooperative processing method and system for visitor unlocking, and the method comprises the steps: detecting that an abnormal visitor inputs wrong passwords for multiple times according to a door lock A, generating an abnormal visitor broadcast message, and obtaining a broadcast notification of the abnormal visitor; according to the abnormal visitor broadcast message received by the door lock B, accumulating the error input times of the lock and the error input times in the broadcast message; according to the fact that a door lock C detects that a normal visitor inputs wrong passwords for multiple times, an abnormal visitor broadcast message is generated, and a misoperation notification of the normal visitor is obtained; according to the abnormal visitor broadcast message received by the door lock A, generating an abnormal visitor exclusion broadcast message, and obtaining an identity confirmation result of a normal visitor; and according to the abnormal visitor exclusion broadcast message received by the door lock C, obtaining a visitor guiding result. By utilizing the embodiment of the invention, the response speed of the intelligent door lock on abnormal condition processing can be improved, and the safety protection capability of a family is further enhanced.

The invention discloses an AI camera system for actively predicting home safety, and relates to the technical field of home safety prediction. The system comprises a multi-sensor data acquisition module which is used for acquiring comprehensive data of a family environment; the AI analysis module is used for analyzing the data acquired by the data acquisition module and identifying abnormal behaviors; the behavior prediction and early warning module is used for predicting possible security threats based on the analysis result of the AI analysis module in combination with real-time data and historical behavior modes; early warning is given out before the threat occurs; the automatic alarm and protection module is used for giving an alarm when potential threats are detected, sending warning information to intelligent equipment of a user and executing protection measures; and the continuous learning and feedback module optimizes algorithms of the AI analysis module and the behavior prediction and early warning module by collecting user feedback and environmental changes so as to improve prediction and protection accuracy.

The invention relates to the technical field of home safety, and discloses an intelligent door lock identification and hierarchical response control method, which comprises the following steps: acquiring multi-modal data in a learning mode to train and construct an authorized behavior baseline model; in the working mode, the current feature vector is extracted in real time, the abnormal score of the current feature vector relative to the baseline model is calculated, when the abnormal score is larger than a threshold value, potential threats are judged, the potential threats are responded, the micro actuator is driven to apply physical disturbance signals, and feedback signals are synchronously collected; analyzing the time sequence characteristics of the feedback signal to discriminate whether the event category of the potential threat is benign or malicious, and executing a hierarchical response strategy; and dynamically correcting the model based on the event category, if the model is benign, updating the authorized behavior baseline model, and if the model is malicious, extracting malicious feature signatures and storing the malicious feature signatures in a high-priority threat signature library. Through active discrimination and closed-loop adaptive learning, the recognition accuracy is significantly improved, and the false alarm rate is reduced.

The embodiment of the present application provides a face detection method, which is applied to the field of face detection, by obtaining a text instruction carrying an image condition, generating an image that meets the image condition according to the text instruction through a preset first large model, training a preset face detection model through a training data set after data enhancement, and in the training process, adjusting the face detection model using a preset staged loss function, generating a target face detection model, obtaining an image to be detected collected by a home security monitoring system, using the target face detection model, detecting the image to be detected, and obtaining a face detection result. The face detection model is trained through a training data set after data enhancement, which improves the richness of the training data and the comprehensiveness of the model training. When facing face recognition tasks under various conditions, the robustness of the model is greatly enhanced, and the accuracy of face detection is further improved through the introduction of a staged loss function.

The invention discloses a home-entry security check gas equipment classification detection method, and relates to the field of equipment classification, and the method comprises the steps: firstly obtaining a field image through a mobile equipment camera, and carrying out the preprocessing and feature extraction of the image through an attention-enhanced visual feature extraction network; secondly, a hierarchical detection head assembly is adopted for equipment recognition, the large class of equipment is detected firstly, and then secondary recognition is conducted on the class needing to be further subdivided; and finally, constructing a gas equipment space relation rule set, and performing context sensing and optimization on an initial detection result based on the rule set. Detection results which are low in confidence coefficient or possibly wrong are corrected by utilizing inherent space and function association between gas equipment. According to the method, efficient, accurate and automatic classification detection of the gas equipment is realized, and the limitation of traditional manual detection and the identification difficulty in a complex environment are remarkably overcome.

The invention provides a smart home security authentication management method and system based on a wireless local area network. The method comprises the following steps: constructing a home network security authentication domain; when a new smart home device is accessed, marking the new smart home device as a second smart home device, and obtaining a device authentication state matrix of a home network security authentication domain; generating an authentication challenge vector based on the device authentication state matrix, and sending the authentication challenge vector to the second smart home device and the first smart home device for cooperative verification; and if the authentication challenge vector passes the cooperative verification, updating keys of all smart home devices in the home network security authentication domain according to a preset dynamic key synchronization protocol. Through construction of a home network security authentication domain, intra-domain sharing of security parameters such as a device authentication state and key management is realized, when any key is updated, other devices update synchronously, and associated dependence exists among keys, so that a risk conduction path of a whole network falling situation caused by key leakage of a single device is blocked.

Embodiments of the present disclosure include systems and methods for communicating information regarding medical events detected at a wearable electronic device. Communications may be transmitted to one or more of a first mobile device, home security system, speaker, second mobile device, medical facility, and emergency response center, for example.

A system and method for providing customized security to cater to different security needs of a people. The system makes private security accessible and affordable to many in society. The system includes an interface configured to allow a service seeker and a service provider to interact with each other. The interface includes a hire screen that is split into an upper section and lower section. The upper section has a map, and the lower section presents a profile of a contractor or service provider. Current locations of the service seeker and provider are presented on the map. Also, the system provides a personal digital assistant implemented on a home security device that allows audio and video telephony with a visitor.

The invention discloses an emergency linkage and emotion recognition integrated household intelligent guard system and method, and relates to the technical field of Internet of Things, artificial intelligence and emergency response, the system comprises the following components: a front-end sensing device, an edge AI processing unit, a voice broadcast interaction module, a remote linkage module and a background data platform; by integrating the front-end sensing device and the edge AI processing unit, multi-dimensional real-time monitoring of human body behaviors, emotional states and environment information is achieved, the abnormal behaviors and emotional states of a user can be accurately recognized by the system through the behavior recognition module, the emotional analysis module and the multi-modal fusion module, and the user experience is improved. And active care and family interaction are carried out through the voice broadcast interaction module, and the remote linkage module is triggered to inform family members or an emergency platform if necessary, so that a closed-loop intervention mechanism is formed, and the intelligent level and the response speed of family safety monitoring are effectively improved.

1. Name of the product in this design: Intelligent Surveillance Camera (HM1009). 2. Purpose of this design: For home security monitoring. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: 3D rendering 3.

An emergency alert device is provided. The device can be used by a young child to alert emergency services and / or a parent / guardian of an emergency. To do so, the device is comprised of at least one button that can be pressed in the event of an emergency. Pressing the button automatically alerts a contact (emergency services and / or a parent) and notifies the contact of the GPS location of the device (and user). In a further embodiment, the device can also be used as a motion sensor for home security and alerting purposes and is comprised of a motion sensor. In this embodiment, the device produces an audible sound when motion is detected.

Determining a sequence for providing a notification regarding activity recorded by a camera is described. In one aspect, a priority sequence for can be determined based on a variety of characteristics of the available devices registered with the home security system of the camera.

Smart Home Security Device with Predictive Threat Detection

The invention discloses an operation system of an accompanying robot, and belongs to the technical field of intelligent robots. The system is composed of a sensing module, an operation control system and an execution operation and maintenance system, and can achieve multiple functions of family safety patrol, pet accompanying care, human emotion interaction and the like. Environmental and target information is obtained through the upper laser radar and the bottom visual AI module, corresponding operation is executed by the execution operation and maintenance system after the environmental and target information is processed by the operation control system, the problem that an existing product is single in function and not intelligent enough is solved, and the system has the advantages of being convenient to use, high in intelligent degree and capable of achieving anthropomorphic emotional interaction.

This application discloses a smart lock system, relating to the field of home security technology. The system includes: a door, with a hidden sensor module integrated inside the exterior door panel; the hidden sensor module includes a radar sensor, a UWB antenna, and a voice receiving module. The radar sensor detects objects approaching the door; the voice receiving module collects sounds around the door; a main control MCU, located inside the door, is electrically connected to the radar sensor, UWB antenna, and voice receiving module; and a motor control system, including a motor driver chip and a DC motor. The input of the motor driver chip is connected to the main control MCU, and the output is connected to the DC motor to drive the bolt. This smart lock system eliminates the need for a front panel, avoiding the problem of exposed panels being easily damaged or having their passwords stolen. While ensuring the smart lock functions normally, it minimizes security risks associated with smart locks.

The invention belongs to the technical field of smart home, and particularly relates to a smart home safety monitoring system and an implementation method thereof.The monitoring system comprises a front-end sensing module, a data processing module, a control module, a communication module, a smart home central control screen and user terminal equipment, and the front-end sensing module and the data processing module are in communication connection; the data processing module is connected with the control module, the control module is connected with the smart home central control screen through the communication module, and the smart home central control screen is connected with the user terminal equipment. The intelligent home central control screen is combined with the safety monitoring system, centralized display and operation of information are achieved, the management efficiency is improved, through comprehensive arrangement of the front-end sensing module, the system can achieve all-directional monitoring of the home environment, it is ensured that no dead angle exists, and the system has the rapid data processing and alarm response capacity.

The application belongs to the technical field of intelligent security and protection home, and relates to a smart floor system capable of identifying falling behaviors, which comprises a smart pressure sensing floor, a data acquisition system and an upper computer, the smart pressure sensing floor is a composite structure of a solid floor and a flexible sensing fabric, the flexible sensing fabric contains 40 electrodes and 40*40 array distributed sensors, and a piezoresistive sensing layer is constructed by a graphene-carbon black mixed filler to form a conductive network; the data acquisition system collects signals through FPC flat cable, transmits data to the upper computer through a sensor router and a public network router; the upper computer adopts a "cloud-edge-end" architecture, carries a machine learning identification algorithm, can accurately identify falling and intrusion behaviors, the falling identification accuracy is more than 98%, and the response time is less than or equal to 1 second; the terminal side is associated with a cloud program to realize alarm pushing and data viewing functions. The disclosed system simultaneously integrates two core functions of old people falling monitoring and home safety protection, and meets the diversified needs of users living alone.

The invention relates to the technical field of smart home security, in particular to a visual doorbell active security scenarized alarm live broadcast method, which comprises the following steps: scene triggering, multi-terminal scenarized notification, user interaction response, danger judgment and conventional interaction ending if no danger exists. If danger exists, active expelling control is executed; alarm live broadcast is started; evidence solidification and feedback are carried out; according to the invention, alarm live broadcast is realized by innovatively combining with a conference: during alarm, on-site videos, position codes and interactive records are automatically synchronized to the conference, real-time check by multiple parties (police, family members, property management and the like) is supported, and the police can intuitively judge the danger level (such as whether an instrument is held or not and whether a door is broken or not) and accurately deploy police strength; and meanwhile, videos and voices in the whole process are automatically recorded and stored, so that a complete evidence chain of'triggering-interaction-expelling-alarming 'is formed. According to the linkage mode, the police decision-making time can be shortened by 50%, the evidence effectiveness is improved by 90%, and the pain points of information lag and evidence fragmentation of traditional alarm are solved.