40 results about "Home based" patented technology

The invention discloses a scene adaptive recommendation method in smart home based on multi-modal data, and relates to the technical field of multi-modal data processing. According to the method, firstly, scene classification is carried out based on multi-mode sensing data for a smart home scene, a current life scene of a user is accurately recognized, meanwhile, an equipment linkage state is analyzed in combination with a classification result, whether equipment operation is matched with a scene requirement or not is judged, and then a scene mode is recognized according to a linkage analysis result to generate preliminary recommendation; whether the dynamic weight space relation model of the user is updated or not is judged, finally, the recommendation effect is quantified according to the weight adjustment judgment result, the recommendation strategy is optimized through two-way feedback, the adaptability to the user requirement is improved, and the user experience is improved. A full-process closed loop of multi-modal data classification, equipment linkage analysis, mode recognition recommendation, weight dynamic updating and effect quantitative feedback in a smart home scene is realized, and the problem that the intelligent degree of scene recommendation in smart home is not high in a user behavior dynamic change scene is effectively solved.

The invention relates to the technical field of smart home, in particular to a smart home collaborative decision and adaptive agent construction method based on LLM. The method comprises the steps of receiving a user instruction to generate a family situation model, decomposing an abstract instruction into a sub-task sequence, identifying conflicts and negotiating consensus, calling a database interaction sub-agent to complete data operation, and injecting a query result to support subsequent task execution and full-link redisk to optimize system performance. Through context sensing, conflict pre-judgment and safety controllable database interaction modules, the problems of fuzzy instruction understanding, dynamic environment adaptation and data safety are solved, the intelligent level and the user satisfaction degree of the system are improved, and meanwhile the sustainable evolution capacity of the system is achieved.

The invention discloses a semi-supervised non-contact neonatal jaundice home intelligent early warning method based on image calibration, and the method comprises the steps: building a camera response function database, and building a color constancy depth model and a skin region segmentation network; designing a strong and weak enhancement strategy by using a large amount of label-free home data, constructing a time and context comparison learning module, and performing self-supervised training on a feature encoder; a small amount of labeled hospital data and a large amount of unlabeled family data are combined, and semi-supervised fine tuning is realized through pseudo label generation and supervised comparative learning; image sequences continuously uploaded by a user are input into the model, multi-day prediction of the bilirubin level is achieved, prediction uncertainty is quantified in combination with a Bayesian method, the risk boundary is dynamically adjusted, and personalized early warning is provided. According to the method, a user does not need to use a physical colorimetric card, color measurement errors caused by model differences of mobile equipment and variability of household illumination are inhibited from a data source, and the accuracy and robustness of subsequent jaundice assessment are improved; through a training framework combining self-supervised pre-training and semi-supervised fine tuning, the understanding ability and generalization performance of the model for the sequential characteristics of jaundice are improved.

Methods and systems are described for controlling access to a home based on user occupancy. According to at least one embodiment, an apparatus for controlling access to a home based on user occupancy includes a processor, a memory in electronic communication with the processor, and instructions stored in the memory which are executable by a processor to determine a location of a user of a home automation system relative to a barrier of a home being monitored by the home automation system. The instructions are executable to determine whether to permit a change in a locking mechanism of the barrier based in part on the determined location of the user, and maintain a current state of the home automation system.

1. Name of the product in this design: Unmanned Aerial Vehicle Countermeasure Equipment (Space-based D100). 2. Purpose of this design: This design is used to cut off the communication and navigation links of drones, forcing them to return to their home base or make an emergency landing, thereby achieving effective control over drones. 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 view 1.

Cleaning robots, comprehensive: a communication interface; at least one sensor; at least one camera (120); a drive unit; and a processor (140) that is configured: to input an image taken by at least one camera into a trained artificial intelligence model in order to obtain information about an object contained in the captured image: to obtain information that is captured by at least one sensor; To obtain recognition information about each of several work surfaces in a home based on information about the object, wherein the recognition information about each of the several work surfaces includes type information about each of the several work surfaces; to generate a map that displays the multiple work areas, using the information obtained about the object, the captured information, and the recognition information for each of the multiple work areas; based on a user voice instructing a cleaning operation to control the drive unit to move to at least one of the multiple work surfaces; and based on moving to the at least one work surface, a cleaning operation for the at least one work surface is to be carried out, where the recognition information for each of the multiple work surfaces includes a name for each of the multiple work surfaces.

1. Name of the designed product: vest (Muse shadow series outerwear home base vest 5318). 2. Use of the designed product: for women to wear. 3. Design points of the designed product: the combination of shape and pattern. 4. Pictures or photos that best show the design points: perspective view. 5. The design points of the top view of the product have been shown in other views, so the top view is omitted; the bottom view of the product is generally not easy to see during use, so the bottom view is omitted. 6. Other circumstances that need to be explained Other explanations: the mannequin is used to support the clothes and does not belong to the protection range of this design.

The invention relates to the field of smart home control, in particular to a smart home control method and system based on voice recognition. The method comprises the following steps: collecting a user voice signal based on an indoor distributed microphone array; performing interference correction on the user voice signal to obtain a voice sequence to be analyzed; performing deep semantic analysis on the to-be-analyzed voice sequence to obtain voice intention information; performing scene semantic prediction on the voice intention information, and outputting a user control demand set; performing security constraint arbitration and abnormal demand elimination on the user control demand set to generate a standardized control demand set; and based on the standardized control demand set, a control instruction is sequentially issued to the smart home, and smart home control operation is completed. According to the invention, efficient and accurate control of the smart home is realized, the user instruction is deeply understood, and the user experience and convenience are improved.

This invention relates to a multi-base station dynamic networking method, system, and device for large-scale venue voting applications. The method includes: monitoring the signal strength between each voting terminal and each base station, as well as the load data of each base station; when the load of any base station reaches a warning threshold, identifying terminals to be redirected; evaluating the marginal increase in communication time consumption of each neighboring base station for the terminal to be redirected, and generating a home selection criterion based on the access adaptation attributes of neighboring base stations; selecting a target home base station from among the neighboring base stations based on the home selection criterion; controlling the load warning base station to suspend the allocation of communication time slots, and coordinating with the target home base station to reserve communication time slots; and issuing a handover command to the terminal to be redirected, instructing the terminal to upload its cache and then migrate seamlessly to the target home base station. Therefore, this invention can adapt to the dynamic distribution of personnel in the venue without manual intervention, fully meeting the stringent requirements for network stability and real-time performance in large-scale conferences.

An object of a system according to an embodiment is to perform adjustment such that a user is at home at the time of delivery.SOLUTION: A system includes a determination unit, a notification unit, and an adjustment unit. The determination unit determines a scheduled delivery time. The notification unit notifies the user of the expected delivery time determined by the determination unit. The adjustment unit performs adjustment so that the user is at home based on the scheduled delivery time notified by the notification unit.SELECTED DRAWING: Figure 1

The application relates to an intelligent home control method and device, electronic equipment and a computer readable medium. The method comprises the following steps: collecting multi-modal data of an intelligent home, wherein the multi-modal data comprises physical device data, environment data and user data; performing data fusion on the physical device data, the environment data and the user data in the multi-modal data to obtain a data fusion result; constructing a virtual model, wherein the virtual model comprises a physical model, an environment model and a user model, and a dynamic feedback mechanism is established among the physical model, the environment model and the user model; training the physical model, the environment model and the user model in the virtual model according to the data fusion result to obtain a digital twin model; and controlling the intelligent home based on the digital twin model and using a self-supervised learning algorithm according to real-time collected multi-modal data of the intelligent home. The application can improve the adaptability of the intelligent home device.

A facility for estimating a future value of a home is described. The facility accesses information about homes in the geographic area including values of attributes for the homes and past changes in valuation of the homes, and aggregate changes in valuation across homes in the geographic area over the same period. The facility creates and trains a model to generate an estimated future rate of appreciation for a distinguished home based on values of attributes for the distinguished home and a forecast rate of appreciation for homes in the geographic area. The facility combines the forecast rate of appreciation based on the home's attribute values and a current valuation of the home to generate an estimated future valuation of the home.

An integrated home energy assessment apparatus, method, and computer program product are disclosed. The apparatus includes one or more processors and non-transitory computer readable storage media storing code. The code is executable by the processors to perform operations that include querying third-party databases for information pertaining to a home and receiving the information from the third-party databases. The operations include analyzing, via machine learning, the information to identify energy-related features of the home. Each of the features corresponds to a scoring category. The operations include determining a lead score corresponding to each feature and aggregating the lead scores within the scoring category to generate a category score. The operations include determining an overall score for the home based on the category score for each of the scoring categories.

This item is a connecting hardware used to connect pieces of wood together. The basic shape of this item is a rectangular steel plate (home base shaped) with five corners. There are holes on the left and right sides of the item for connecting pins (or bolts) to unite it with the wood. There is a multipurpose hole on the bottom of the item for multipurpose use. When using this item, a cut of the same thickness as the item is made into the wood and fixed with a connecting pin (or bolt).

The application discloses a pension agency fire risk prevention and control and operation decision system based on a knowledge graph, relates to the technical field of fire data processing and management of a pension agency, and obtains a central risk node by acquiring initial fire source position data and mapping; a first risk influence subgraph with the central risk node as the center is acquired; a passing risk coefficient is calculated through cyclic iteration, the first iteration is used for correcting a basic passing coefficient according to the difference in spatial geometric characteristics to calculate an environmental risk coefficient, the non-first iteration is used for correcting the passing risk coefficient of the last iteration according to the difference ratio of the environmental risk coefficient, and the iteration is continued until the convergence condition is met; and a prevention and control decision report is output according to the converged passing risk coefficient. The application realizes dynamic iterative evaluation and self-adaptive path decision of fire risk, and through iterative propagation and dynamic correction of the risk gradient, the evacuation path can respond to the evolution law of the fire environment in real time.

The application discloses a home base station switching method, device, equipment and medium. The method is applied to a macro base station and comprises the following steps: obtaining home base station information in capability information of an access user equipment; when receiving a switching measurement report of the user equipment, issuing a measurement instruction to the user equipment, and switching the user equipment into a target home base station corresponding to the home base station information according to the home base station information and the measurement report fed back by the user equipment. The home base station information is written into the capability information of the user equipment, so that the information of the target home base station can be obtained from the capability information of the user equipment when the user equipment is switched from a macro base station cell to a home base station, and the switching process is completed. Therefore, the switching can be completed without configuring adjacent area and adjacent station information in the macro station, that is, without establishing an SCTP connection, so that the problem that the adjacent area of the macro base station is limited is avoided. The automatic switching of the home base station is realized, and the experience of users is improved.

This application provides a method and system for controlling environmental parameters in smart homes based on multi-source data fusion. The method includes acquiring environmental and structural parameters of a target topological zone in the current time period, then acquiring the actual mold level of the target topological zone in the current time period, determining the base airflow corresponding to the target topological zone, then determining the additional airflow corresponding to the target topological zone based on the environmental and structural parameters, then determining the total control airflow of the target topological zone in the current time period based on the base and additional airflow, and finally controlling the ventilation equipment corresponding to the target topological zone to operate according to the total control airflow. Compared to the current smart device linkage control method that simply links mold detection with the indoor ventilation system, this method can meet the refined and intelligent mold suppression needs of smart homes, solving the industry pain point that traditional methods are unable to effectively suppress mold in large areas of the interior.

The application provides a smart home multi-modal edge AI deep learning autonomous control method and system. The method comprises the following steps: storing the multi-modal multi-dimensional data set of the smart home device to the gateway locally after encryption processing, generating a space-time feature matrix after preprocessing, constructing a personalized control portrait combined with control identity data, analyzing and processing through a preset home scene recognition model according to the space-time feature matrix, obtaining home scene category feature data, obtaining historical behavior sequence feature data, predicting corresponding predicted behavior demand data combined with the personalized control portrait, finally executing smart home autonomous control, and evaluating device abnormal conditions and performing abnormal root cause tracing; the application stores data through an edge gateway, constructs a space-time feature matrix and a personalized control portrait, identifies a home scene category, accurately predicts behavior demand, and ensures that the abnormal root cause can be traced, thereby realizing autonomous control of the smart home based on multi-modal edge AI deep learning.

A method comprises receiving (101) user information, determining (103) that a user is away from home based on the user information, and receiving (105) light information from a lighting system which comprises a light source. The light information is indicative of the state of the light source. The method further comprises determining (107), based on the light information, that the light source is on while the user is away from home, determining (109) what caused the light source to be turned on if it has been determined that the light source is on while the user is away from home and deciding (111) whether or not to notify the user in dependence on the cause after the cause has been determined. After the decision has been made, the notification is transmitted (113) to a user device of the user in dependence on the decision.

The application discloses a kind of intelligent home voice security test method and device based on voiceprint anti-counterfeiting, it is related to intelligent home security test technical field.The core of the device includes main control module, attack simulation module and response acquisition module.Attack simulation module generates multi-dimensional voice deception attack sample including replay, synthesis, conversion and countermeasure sample.Main control module is built-in dynamic countermeasure reinforcement learning algorithm, and dynamically generates digital domain control instruction and physical domain control instruction according to the response state of the device to be measured, wherein the physical domain control instruction directly drives the stepper motor of physical replay sub-module to carry out angle rotation, realizes the deep binding of algorithm strategy and physical hardware action.Response acquisition module comprehensively captures the state feedback of the device to be measured by multi-modal perception means.The device can further include environment simulation module and evaluation and output module to construct physical test space with specific acoustic characteristics and output quantitative evaluation report.The application first proposes a comprehensive test device combining software and hardware from the perspective of active attack test, which can comprehensively and automatically evaluate the voiceprint anti-counterfeiting performance of intelligent home voice control system and Internet of Things devices.

This application relates to a control method, system, terminal, and storage medium for crossing power transmission lines, belonging to the technical field of drones crossing power lines. The method includes: acquiring preset power transmission line tower information, including the connection height, tower spacing, and tower location; acquiring drone flight information based on the power transmission line tower information, including a preset flight path and preset flight altitude; acquiring the drone's real-time flight altitude; determining whether there is a real-time flight altitude that does not match the preset flight altitude; if so, acquiring a return-to-home prompt instruction; and controlling the drone to return to home based on the return-to-home prompt instruction. This application has the effect of timely detecting situations where the drone's tow rope detaches, reducing wasted time and lowering drone battery consumption.

The invention belongs to the technical field of health monitoring, and particularly relates to a senile dementia risk home monitoring method based on multi-modal behavior analysis, which comprises the following specific steps: monitoring human body postures in real time, analyzing repeated actions, synchronously early warning abnormal movement, and carrying out voice interaction operation through a built-in AI dialogue terminal; performing behavior, emotion and physiology correlation modeling, executing cognitive decline dynamic evaluation through cognitive function self-test, and judging a risk level according to an analysis and evaluation result; executing a multi-level early warning mechanism based on the risk level information, and automatically taking corresponding emergency measures; the threshold value is dynamically calibrated by integrating user habit data, a closed-loop feedback mechanism is designed to realize false alarm suppression, and a monthly health report is synchronously generated to assist a doctor in adjusting an intervention scheme. The problems that in a traditional monitoring mode, tiny behavior changes are difficult to capture, rescue is delayed due to subjective evaluation hysteresis and passive response, the screening frequency is low, and the scene is limited can be solved.

The invention provides a smart home control method and system based on the Internet of Things, and belongs to the technical field of intelligent control, and the method comprises the steps: constructing a communication topology network of a plurality of groups of sensors and edge nodes; performing data analysis on the received environment data based on the edge nodes, constructing a single-point detection layer based on each sensor, and performing diffusion analysis on position points of non-deployed sensors on the single-point detection layer according to the corresponding detection type and the circulation condition of each communication channel of the preset area to obtain a complete detection layer of each sensor; and performing global analysis on all the complete detection layers to obtain an initial control scheme of the preset area, performing adjustment according to an area scene of the preset area and a user demand to obtain an intelligent control scheme, and controlling the corresponding smart home in the preset area to execute control operation. The problems of low control precision and poor user experience are solved.

The invention discloses an EEG-based old-age institution visual environment cognitive performance evaluation method, relates to the technical field of environment monitoring, and aims to solve the problem that the cognitive performance of a scheme cannot be effectively pre-judged and optimized in the prior art. Objective quantitative evaluation of the cognitive performance of the visual environment of the pension institution is achieved, the problem that a traditional design evaluation method is low in evaluation accuracy is solved, and then effective pre-judgment and optimization of the cognitive performance of the scheme are achieved.

1. Name of the product in this design: Home Base Station (NVR W2000). 2. Purpose of this design: To be used as a home base station, home wireless router, home gateway, and other devices to enhance network coverage. 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 view 1.

This invention discloses a method and system for stylized design of smart homes based on generative adversarial networks (GANs), relating to the field of computer-aided smart home design. It employs a multimodal fusion encoder to encode and fuse stylized design information, extracting text, image, and structural features respectively, and then deeply fusing them through an attention mechanism to generate conditional guidance vectors containing spatial layout and stylistic features. These conditional guidance vectors are then input into a pre-trained dual-channel GAN. This invention solves the problem that existing AI-powered smart home stylized designs can only generate uneditable visual images, achieving simultaneous generation of visual effects and structured design data, thus improving the practicality and feasibility of the design results. Through a dual-channel generation architecture and differentiable rendering consistency constraints, it ensures precise alignment between visual images and structured data, improving design accuracy and reliability.

The application relates to the technical field of smart home, and discloses an Internet of Things smart home control method and system based on personalized services, which comprises the following steps: collecting bioelectric signals, environmental reflection signals, environmental point cloud data and light intensity distribution data of a user on a smart home; performing space-time coding processing on the bioelectric signals to obtain a biological feature vector, coding the environmental point cloud data and the light intensity distribution data into a behavior mode vector, and extracting a space thermal field corresponding to the environmental reflection signals and the environmental point cloud data; establishing a topological relationship graph among the smart homes, constructing a cross-device collaborative strategy among the smart homes based on the biological feature vector, the behavior mode vector and the topological relationship graph, and determining a real-time control instruction of the smart home. The application can deeply integrate multidimensional biological and environmental signals, establish intelligent topological correlations among devices, and effectively utilize space energy information for collaborative decision-making and accurate control.

A method includes: receiving (101) user information, determining (103) that the user has left home based on the user information, and receiving (105) light information from a lighting system including a light source. The light information indicates the state of the light source. The method further includes: determining (107) that the light source was turned on when the user left home based on the light information; determining (109) a reason for the light source being turned on if it has been determined that the light source was turned on when the user left home; and, after determining the reason, deciding (111) whether to notify the user based on the reason. After making the decision, transmitting (113) a notification to the user's user equipment based on the decision.

This invention discloses a method and system for early warning of abnormal outings by elderly people living at home based on a stay probability model. It relates to the fields of smart home, elderly monitoring, and behavior recognition technology. The method includes: acquiring historical monitoring data of the ward in a sensing area under conditions without prior warning, and calculating the ward's baseline personal behavior data; real-time monitoring of whether the ward enters the sensing area, and acquiring real-time monitoring data after entering the sensing area; based on the baseline data and real-time monitoring data, comprehensively analyzing anomalies in stay duration and outing hours using a stay probability model, and calculating the comprehensive stay probability for this stay; and executing a tiered early warning strategy based on the comprehensive stay probability for this stay. This invention can learn the daily behavioral habits of the elderly without them noticing, quantitatively assess the risk of abnormal outings, and provide tiered early warnings ranging from mild reminders to emergency alerts. Its core objective is to achieve early detection, early intervention, low false alarms, and high acceptability.