1694 results about "Older people" patented technology

The invention discloses a method for correcting dialogue intention information of old people based on a deep neural network, and particularly relates to the technical field of dialogue optimization, and the method comprises the steps: collecting and cleaning multi-topic multi-round dialogue data, marking multi-round dialogue corpora, and unifying the format; performing feature extraction on the labeled corpus by using the multi-intention recognition sub-model to generate a multi-intention representation vector capable of representing each intention; screening context content related to the target intention in combination with a hierarchical attention mechanism of user context dynamic weight adjustment; processing context content related to the target intention through gating operation based on multiple rounds of interaction history, and reserving semantic fragments with reference value; performing parallel analysis on the reference semantic fragments based on a multi-task learning structure of a deep neural network, performing classification processing on different intentions, and calculating scores; and executing multi-intention fusion correction operation according to classification and scoring results, and outputting intention information capable of distinguishing each intention.

The invention discloses an old people tumble alarm system and method based on multiple sensors, and relates to the technical field of intelligent monitoring, and the system collects the body data of old people through an intelligent bracelet and a sensor group, transmits the body data to a cloud platform through a wireless network, carries out the processing, and generates a state data group. The acceleration modulus length, the angular velocity modulus length, the gait cycle and the posture angle of the old people are calculated, so that the motion state and the falling risk are accurately judged, a falling risk assessment coefficient is calculated based on the data, old people falling assessment is performed according to the falling risk assessment coefficient and a preset falling risk threshold value, and an alarm is triggered and a guardian is notified when the risk exceeds the standard. The heart rate variability index and the blood oxygen content are analyzed, a comprehensive health index is obtained through calculation, possible body injury after falling is evaluated, and injury alarm information is generated. The system can accurately monitor the health state of the old people in real time, timely send out early warning and alarm information, and provide comprehensive fall prevention and health monitoring guarantee.

The invention provides an integrated sharing method and platform for health data of old people, and relates to the technical field of data processing.The method comprises the steps that multi-source heterogeneous health data of the old people are accessed, a rule template in a historical grading preprocessing rule base is matched based on data source capacity and data features, and data cleaning and standardization are completed; further identifying a sensitive field based on a medical data standard and an industry privacy specification, generating a sensitive field vector, carrying out sensitive level scoring, and calling a data desensitization strategy library to execute desensitization operations such as de-identification and masking; an access control function is constructed based on user attributes, access context and field sensitivity levels, authorization levels are determined, differential encryption is performed, and authorization views and data tokens are generated for secure access. Through the method, the problems of heterogeneous sources, complex processing, insufficient privacy protection and lack of flexibility of a sharing mechanism of old-age health data in the prior art can be solved.

The invention relates to a model for evaluating and predicting mild cognitive impairment risk of old people in a pension institution. The model sequentially comprises a behavior analysis module, a language recognition module, a social modeling module, a toughness calculation module, a feature fusion module, a risk reasoning module and the like. Behavior deviation characteristics and abnormal time periods are extracted by collecting behavior data of daily life, diet, social contact and the like of old people and comparing the behavior data with an institution work and rest template; in combination with nursing records, extracting language anomaly features; analyzing social frequency and structure changes in the abnormal time period, and extracting social variation features; a cognitive toughness index is calculated by integrating the health archive and the recovery ability to the health event; and performing toughness weighting on the multi-dimensional features to construct a time sequence tensor, and inputting the time sequence tensor into a recursive model to predict a cognitive impairment risk value. And if the risk value suddenly changes, the system automatically backtracks the feature trajectory of nearly 7 days, constructs and screens a prediction path with the strongest interpretation force, outputs a dominant prediction result and a key factor sequence, and realizes high-interpretability and high-reliability early recognition and intervention reference.

The invention discloses a platform and a monitoring method for monitoring the home safety of old people based on decision engine multi-device linkage, and the method comprises the steps: carrying out the data preprocessing according to the human body home data collected by an edge computing device; according to the human body home data, multi-modal data fusion is carried out to generate behavior identification data, and the multi-modal data fusion comprises one or more of time sequence alignment, modal completion and feature extraction; inputting the behavior recognition data into a local recognition model for real-time behavior analysis, and inputting the behavior recognition data into a cloud decision engine for multi-source data comprehensive deep analysis; and according to an output result of the local identification model and / or the cloud decision engine, performing operation of matching the smart home equipment and / or home behavior alarm processing. According to the invention, multi-device data integration and cooperative linkage measurement can be adopted to monitor the home safety of the elderly.

The invention belongs to the technical field of information technology and intelligent health service, and discloses a community old-age care service resource optimal configuration method based on edge computing, which comprises the following steps: collecting physiological data, environmental data and activity data of old people to form original heterogeneous data, uploading the original heterogeneous data to a data warehouse through an urban and rural community network adaptation mechanism, generating a space-time tagging multi-modal data set; constructing a dynamic digital twinborn model, generating a space thermodynamic diagram, identifying different level priority requirements, and generating a time period allocation scheme through a conflict priority scoring mechanism; an MILP model is constructed to dynamically optimize edge node computing power and functional area space resources, and a resource optimization report and a block chain transaction list are generated; desensitizing the original data of the old people, creating a digital identity card, generating an encrypted service log, and synchronously recording migration behaviors of the old people; and triggering network strategy switching and priority threshold self-adaption, and carrying out resource scheduling and strategy closed-loop optimization.

The invention discloses an old people falling risk assessment and dynamic protection intervention system and method, and relates to the technical field of computer vision, and the system comprises a hardware composition module, a software and algorithm module, an intervention mechanism module, a system integration and optimization module, and an application scene module. The hardware composition module is used for collecting motion postures, gaits and environment data of old people in real time through a multi-mode sensor, hardware support is provided for risk assessment and protection, deep fusion of vision, inertia and environment data is achieved through an attention mechanism, spatial-temporal features are rapidly extracted in combination with a lightweight CNN-LSTM network, and the accuracy of risk assessment and protection is improved. And then the dynamic relationship between the mass center and the supporting surface is accurately calculated based on a biomechanical model, so that the real-time performance of attitude analysis and the accuracy of fall risk assessment are effectively improved, the prediction accuracy of the system on the fall risk of the old people is greatly improved and the false alarm rate is reduced while the consumption of calculation resources is reduced, and a reliable basis is provided for a hierarchical protection strategy.

The invention discloses an old people cognitive ability assessment system and method based on multi-modal fusion, and relates to the field of old people cognitive assessment, and the method comprises the steps: firstly obtaining the current multi-modal data of a user, and fusing the current multi-modal data into a current feature vector; then, instead of being compared with the universality standard, the personal baseline portrait of the user is called, and the score of the difference degree between the current state and the historical baseline of the user is calculated; particularly, the core index of the drift speed is introduced, and the difference degree and the historical change rate are combined, so that quantitative modeling is carried out on the changed speed. By analyzing the amplitude and the speed of the change at the same time, normal aging with gentle change and low speed and pathological recession with violent change and high speed can be effectively distinguished, so that the key technical problem that the normal aging and the pathological recession are easy to be confused in the background technology is accurately solved.

The invention discloses an old people asthenia phenotype evaluation and nursing plan making method and device and a medium, and relates to the field of asthenia evaluation.The method comprises the steps that physiological health data, psychological state data and social interaction data of old people to be evaluated are collected, key features are extracted, and a plurality of key indexes are obtained; generating a weakness phenotype evaluation report (including a physiological evaluation result, a psychological evaluation result and a social interaction evaluation result) of the to-be-evaluated old people according to the plurality of key indexes; generating a nursing plan (including medical suggestion measures, psychological support suggestion measures and social activity suggestion measures) of the to-be-assessed old people according to the weakness phenotype assessment report; and nursing the to-be-evaluated old people based on the nursing plan, collecting health feedback data according to a set period, and optimizing and adjusting the nursing plan according to the health feedback data. According to the method and the device, the efficiency and the precision of asthenia phenotype evaluation of the elderly are improved, the personalized nursing plan is generated, and the nursing effect of the elderly is improved.

The invention discloses an old people cognitive health monitoring system based on virtual reality, and relates to the related technical field of virtual health monitoring technologies. Comprising a multi-modal sensing acquisition module, a virtual reality environment generation module, a cognitive nerve feedback module, a real-time feedback module, a deep learning analysis module, a personalized training plan generation module, a data storage and management module, a remote medical platform and a virtual assistant and coach module. The system can help the elderly to improve the work memory ability through a special work memory training task and a cognitive nerve feedback module, and the elderly can be more efficient during daily life activities by improving the work memory and the attention basic cognitive function aiming at the common attention distraction problem of the elderly, so that the life quality is improved, and the elderly can be helped to take care of the elderly. The cognitive neural feedback module not only pays attention to a cognitive function, but also helps the elderly to better manage emotions by adjusting an emotional network, promotes social interaction, and reduces aloneness and anxiety.

The invention provides a remote elderly monitoring method and system based on artificial intelligence, and relates to the technical field of elderly behavior recognition, and the method comprises the steps: firstly obtaining a current image and a physiological monitoring signal of an elderly in a target area; preprocessing the current image and the physiological data to obtain a preprocessed image and the physiological data; performing multi-modal fusion on the preprocessed image and the physiological data based on a fusion algorithm to form fusion data; inputting the fusion data into a trained old people behavior model to obtain a monitoring result; the model is constructed through a walking elderly sample set and a wheelchair elderly sample set. And giving an alarm according to a monitoring result. According to the method, the multi-modal information of the image and the physiological signal is combined, the visual information and the physiological state are comprehensively considered, and the monitoring accuracy and reliability are greatly improved. The complementarity of different source information is effectively utilized by multi-modal fusion, and the behavior recognition capability in a complex scene is enhanced. By considering individual difference information, false alarm and missing alarm can be reduced, and the monitoring effect is improved.

The invention relates to the field of information technology and intelligent old-age care service, and discloses an old-age care management system based on collaborative recommendation and a multi-dimensional situation, which comprises a data acquisition module, a situation perception module, a collaborative recommendation module and a real-time feedback and dynamic adjustment module, the data acquisition module acquires health data of old people in real time through the smart bracelet, the situation sensing module establishes a blood pressure fluctuation prediction model according to the acquired health data and situation data of the old people and predicts the risk of blood pressure fluctuation, and the collaborative recommendation module establishes a dynamic situation adjustment blood pressure monitoring model and predicts the risk of blood pressure fluctuation. The real-time feedback and dynamic adjustment module dynamically combines real-time measured blood pressure data and situation data, comprehensively reflects health risks of the old people under different environmental conditions, more accurately reflects actual health conditions of the old people, generates dynamically adjusted blood pressure values, and feeds back the blood pressure values to the real-time feedback and dynamic adjustment module according to the adjusted real-time blood pressure data of the old people. And managers and old people are helped to make reasonable health management strategies.

The invention relates to the technical field of nutrition and health status assessment, and particularly discloses a deep learning-based old people nutrition and health status assessment system and method, and the method comprises the steps: obtaining all nutrition and health status assessment indication information of a to-be-assessed old people object; a data processing and analysis algorithm based on artificial intelligence and deep learning is introduced to a back end to perform embedded semantic aggregation analysis on the various nutrition and health state assessment indication information, and then state assessment is performed based on the health state comprehensive feature representation of the elderly object to determine a state assessment result. Therefore, the quality of the nutrition and health state evaluation of the old people can be improved through a more intelligent data processing mode, and the score is not based on a fixed threshold but is automatically generated through a model, so that the difference between different regions or groups can be more flexibly reflected, meanwhile, the biological variation between individuals is also considered, and the evaluation accuracy is improved. Therefore, personalized risk prediction in a real sense is realized.

The invention provides a system and a method for evaluating the independent living ability of old people. The method comprises the following steps: acquiring multi-modal original data (including physiology, behavior and interaction data) of target old people; performing modal coding, query vector interactive fusion (including missing information completion) and feature compression on the multi-modal original data to obtain a standardized feature vector; a first-layer model evaluates behavior and cognitive ability indexes, and a second-layer model combines individual background information to predict a living ability grade and a degradation risk; optimizing the first-layer model through a feature feedback mechanism; and finally, outputting and visualizing a structured evaluation result. The method breaks through the limitation of traditional manual evaluation, achieves the deep fusion of multi-modal data, improves the objectivity, robustness and interpretability of evaluation, and can meet the precise evaluation demands of home care, community monitoring and smart medical scenes.

The invention discloses an elderly emotion evaluation system based on multi-modal physiological and behavior signals. The elderly emotion evaluation system is composed of a multi-modal signal acquisition module, an emotion induction task design module and a multi-modal feature processing module. By collecting multi-mode signals such as electroencephalogram, electrocardio, voice and facial expressions, the system can synchronously record physiological and behavior changes of a user in a specific situation. The emotion induction task adopts scientifically designed audio and video contents, and six typical emotions such as happiness, tension, boring, anger, calm and sadness are guided. Then, the system extracts emotion related features from the multi-modal signals by using a deep learning method, models a cooperative relationship among the multi-modal signals by using an attention mechanism fusion technology, and generates unified emotion feature representation; by comprehensively analyzing and identifying the emotional state of the elderly user, the system provides important reference for early monitoring of emotional health of the elderly, psychological state evaluation and personalized intervention, and has social and clinical values.

The invention discloses an auxiliary decision-making system for cognitive competence assessment of old people, which relates to the technical field of cognitive auxiliary decision-making and comprises an environmental noise acquisition and feature extraction module, a time sequence fluctuation analysis module, a dynamic threshold reconstruction and judgment module, a cross-domain consistency calibration module, a multi-modal synchronous verification module and a dynamic threshold regulation and control module. According to the method, the environmental noise is dynamically analyzed, so that the defects of a fixed noise suppression threshold and a static feature extraction model in the traditional technology are overcome. A sound field energy distribution model is constructed in real time and time sequence fluctuation analysis is combined so that a noise fluctuation interval can be identified, a voice starting point detection threshold value is dynamically adjusted and evaluation precision is enhanced. Through multi-mode synchronous verification combining a reaction time curve and a facial movement track, a time offset error is corrected, a judgment standard is adjusted in real time according to noise changes, misjudgment and virtual high risk early warning are avoided, and therefore the accuracy and reliability of cognitive ability assessment of the old are improved.

The invention, which relates to the field of the intelligent accompanying system, discloses an interactive digital person accompanying system for the old with cognitive impairment, comprising a data acquisition module, an emotion recognition module, a first matching module, a second matching module, a personalized interaction generation module and a reinforcement learning adaptive module. The data acquisition module acquires multi-modal sensing data of a target old person; the emotion recognition module outputs an emotion label and an intensity value through feature extraction and Transform fusion; a first matching module screens corpus fragments according to emotion intensity triggering conditions; the second matching module performs secondary screening based on the retrieval weight; the personalized interaction generation module generates digital twin interaction data through probability distribution sampling; the reinforcement learning adaptive module dynamically adjusts the corpus weight according to the interaction reward value; according to the method, the dual guarantee of emotion guarding and physiological safety is realized, the anxiety emotion of the old with cognitive impairment is remarkably relieved, and the novelty of interaction and high emotion relieving efficiency can be kept.

The invention discloses an elderly falling early warning and intervention method based on emotion recognition and behavior pattern analysis. The elderly falling early warning and intervention method comprises the following steps: S1, collecting a facial expression data set, a voice signal data set and a behavior data set of an elderly; s2, analyzing the collected facial expression data set by using a convolutional neural network model; s3, analyzing the voice signal data set by using a long short-term memory network model; s4, an ElderSense model is set; and S5, when the analysis result shows that the risk score is higher than a preset first threshold value, sending an early warning signal to the old people through the intelligent equipment, and when the analysis result shows that the falling risk is higher than a preset second threshold value, sending an early warning signal to the old people and caregivers thereof through the intelligent equipment. The falling risk of the old people can be comprehensively evaluated from multiple angles, and the accuracy of falling risk judgment in a complex situation is improved.

The invention discloses a waistband type falling protection early warning device for old people living alone, and belongs to the technical field of intelligent wearable equipment. The problems that an existing device is single in early warning mode and cannot effectively guarantee the safety of old people living alone are solved, corresponding action results are matched for various data of the old people through the convolutional neural network and the lightweight decision tree model, and if a preliminary judgment result conforms to a normal mode, a judgment result is directly output; if not, triggering a secondary analysis mechanism to further judge whether the old people fall down or not; the GPS positioner is also used for analyzing whether falling behaviors occur at the same position for many times, and if a high-risk position is found, when the old people approach the high-risk position, the warning lamp and the alarm are used for reminding and paying attention to action behaviors; the fall detection accuracy is improved, the possibility of false alarm and missing alarm is reduced, the old people can be actively prevented from falling again in a high-risk area, and a more comprehensive safety guarantee is provided for the old people living alone.

The invention provides a hearing screening system suitable for rural old people, and belongs to the technical field of data processing, and the system comprises a data collection module which collects voice and determines a comprehensive data set; the equipment calibration module is used for determining an audio output parameter of the hardware equipment based on the hardware equipment characteristics in combination with the hardware equipment characteristics, and calibrating the hardware equipment; the parameter determination module is used for determining voice interaction parameters; the hearing test module is used for generating an audiogram; the preliminary judgment module is used for analyzing the audiogram and determining a preliminary judgment result; and the result determination module is used for selecting vocabularies from a preset vocabulary database and a dialect voice database, playing high-frequency audios corresponding to the vocabularies on the calibrated hardware equipment, obtaining user feedback and determining an optimization judgment result in combination with the vocabularies and the dialect voice database. The method has the advantages of individuation, accuracy and high efficiency, is low in cost, is especially suitable for hearing screening requirements of rural old people, and improves the universality and accuracy of screening.

The invention relates to the technical field of health assessment, in particular to a psychological health assessment system and method for old people, and the system comprises a psychological monitoring module, a psychological analysis module, an adjustment strategy module and an intervention execution module. According to the method, the behaviors of the old people are monitored, data are recorded, abnormal indexes of the psychological state are accurately analyzed, the behaviors are matched with the psychological state through the psychological mode atlas, the accuracy of psychological health assessment and the response speed to changes are greatly improved, and the psychological health can be effectively monitored and timely adjusted by adjusting assessment parameters in real time; after the optimized psychological state data is deeply analyzed, the tuning effect can be more accurately evaluated, personalized psychological intervention measures are provided for the elderly, the personalized degree of the intervention measures is enhanced, the individual requirements are better met, and by analyzing feedback in the intervention period and recognizing key change points, the optimization effect is improved. And the accuracy and the effect of mental health intervention are obviously improved.

The invention discloses a remote monitoring robot for health monitoring of old people, and relates to the technical field of monitoring robots, the remote monitoring robot comprises a mobile robot module, and the mobile robot module is in communication connection with a deep learning algorithm module, a voice module, an anomaly detection module, a positioning module and an emergency safety guarantee module. According to the remote monitoring robot for health monitoring of the old people, by integrating a deep learning algorithm and a mobile robot technology, a safe living space is provided for the old people, activities of the old people are monitored in real time, and it is ensured that the old people can be timely helped when encountering health crisis through a fall-down detection and emergency situation feedback mechanism; when it is detected that the old man falls down through a YOLOv5 algorithm, help seeking information is immediately sent to a preset guardian, in addition, the mobile following function of the robot provides continuous monitoring when the old man goes out, and the robot can closely follow the old man at home or outdoors to ensure that no dead angle exists in monitoring.

The invention discloses an emotional interactive intelligent nursing system and method based on multi-modal perception, and belongs to the technical field of intelligent medical treatment and intelligent old-age care crossing. The matrix type pressure sensor array is integrated in the nursing mattress, multi-point pressure distribution of the body of the old people on the bed surface is obtained in real time, and high-resolution body pressure mapping is achieved. Compared with an existing single-point type or linear pressure monitoring structure, the system can accurately recognize the spatial position of a local pressed area, and early recognition and quantitative evaluation of a bedsore risk area are achieved. By fusing the pressure matrix, the heart rate, the blood oxygen, the environment temperature and humidity, the urine wetting and other multi-mode sensing data, the monitoring dimension is remarkably expanded, the health condition can be comprehensively evaluated from multiple physiological indexes, compared with a single physiological parameter monitoring mode, the more comprehensive information obtaining capacity is achieved, and the monitoring precision and the nursing efficiency are effectively improved.

The invention discloses a household elderly falling monitoring system and method based on feature fusion, and relates to the technical field of falling monitoring. The technical key points are as follows: firstly, the reasonability and effectiveness of an obtained detection result are guaranteed to obtain first gait image recognition of the home elderly to estimate the posture of the home elderly, the improved OpenPose network and CoordConv are utilized to replace VGG-16 into EfficientNet, the first gait of the home elderly is accurately monitored, the balance influence proportion is calculated for different gaits, and the balance influence proportion is calculated for the different gaits. According to the method, the probability that the old people have falling behaviors is judged to generate different risk signals, falling behaviors and postures under medium and high risk signals are further analyzed in a targeted mode, through deflection analysis, changes of human body postures are monitored in real time, characteristics of different falling postures are understood, different falling postures are independently and accurately modeled, and a second falling coefficient is generated; the tumble degree is accurately identified, and a foundation is laid for optimization of a subsequent model.

The invention discloses an intelligent old-age care monitoring system convenient for monitoring vital signs of old people, and belongs to the field of intelligent old-age care. The system comprises a monitoring terminal, a data service and a monitoring terminal, the monitoring terminal part is used for monitoring and collecting vital sign information and exercise intensity information of the old and sending the information to the data service part; the data service part is used for processing the information data collected by the monitoring terminal part, generating an old person monitoring index and notifying an optimal monitoring person after the monitoring index is abnormal; and the monitoring terminal part is used for monitoring the vital sign information of the coming person. According to the invention, when the monitoring index is generated, the exercise intensity index is introduced, so that the occurrence of an abnormal condition of false alarm caused by the change of vital sign information data when the elderly take exercises can be avoided, and the accuracy of monitoring can be ensured. And after the monitoring index of the old person is abnormal, the optimal monitoring personnel is notified to go for processing, so that the timeliness of emergency processing is ensured.

The invention discloses a method and system for guiding an old person to go home through a door lock, and the method comprises the steps: calculating a lost score value through a lost score model according to a condition that a target door lock A detects that the old person fails in continuous verification for multiple times and stays overtime in front of the door, and judging that the old person is in a lost state when the score value exceeds a threshold value, and obtaining a lost judgment result; according to the door lock A, encrypting and broadcasting the elderly biological characteristics to all the door locks, receiving confirmation information returned by the successfully matched door lock B, and obtaining a target room positioning result; according to the floor topological data of the door lock A and the door lock B, generating an optimal guide path, and obtaining path data containing a passing door lock sequence; according to the path data, controlling lamp strips along the way to be lightened according to a preset mode, and cooperating with a directional sound box to play voice guidance; and sending a reset instruction to the path door lock according to the fact that the door lock B detects that the old person successfully unlocks or arrives. According to the embodiment of the invention, convenient, efficient and intelligent door lock guiding can be carried out, and precise guiding and supporting for the elderly are realized.

The invention discloses an old-age care institution safety risk management and control system based on a digital twinning technology, and relates to the technical field of digital twinning application old-age care safety risks, and the system can perform fusion analysis on video images, meteorological data, old-age behavior characteristics and other multi-dimensional data in an old-age care institution, and accurately identify factors which may cause risks. Through analysis of video images, the system can identify dangerous characteristics such as ground water spots, glass doors, sockets and the like and associate the dangerous characteristics with behavior characteristics of old people. And the gestures and gaits of the old people are monitored, so that the risk of falling or slipping is judged, and a falling coefficient Sd is calculated in real time. For an accident, the system monitors changes in the environment, such as sudden object movement or falling sound and noisy sound between the elderly, and calculates an accident coefficient Yw. Different from a traditional universal alarm system, the scheme triggers an alarm mechanism in a targeted manner according to the specific environment condition of the old.

The invention belongs to the field of nursing robots, and particularly discloses a multi-robot collaborative operation method and system for home nursing of old people, and the method comprises the steps: building a virtual simulation platform which comprises a simulation robot, the old people and a home environment, a large motion planning model built based on the PaLM-E is deployed on the simulation robot; the multi-modal information comprises visual sense, touch sense, sound and temperature sensing information; multi-robot cooperative operation training is carried out based on a virtual simulation platform, so that the action planning large model is trained; multi-modal information and nursing task information about the old people are obtained and input into the trained motion planning large model, unit motion planning of all robots is obtained, and home nursing of the old people with multi-robot collaborative operation is achieved. According to the invention, personalized, efficient, comprehensive, safe and intelligent nursing services can be provided for old people.

The invention relates to the technical field of big data tourism management, in particular to an old people tourism safety management method and system based on big data. Obtaining an estimation result corresponding to each tourist output by the tourist missing estimation model through the tourist missing estimation model and the actual tourism information corresponding to each tourist, marking a user terminal worn by the tourist whose estimation result is estimated to be lost as a first target terminal, and then marking other user terminals as second target terminals; then the tour guide terminal obtains a first sound signal sent by the first target terminal and a second sound signal sent by the second target terminal; and the tour guide terminal determines the missing risk level of the tourist wearing the first target terminal based on the first sound signal and the second sound signal, so that the tourist with the high missing risk can be warned in time, and the situation that the old people are lost when traveling along with the group can be avoided as much as possible.

The invention discloses a method for predicting an abnormal behavior state of an elderly living at home based on digital twinning, and the method comprises the steps: obtaining a social relation network data flow and a non-intrusive energy consumption data flow of the elderly living at home, and constructing a time sequence dynamic social relation graph based on the social relation network data flow, meanwhile, a semantic activity event chain is generated based on energy consumption data flow overload decomposition, cross-modal fusion processing is carried out on the social relation graph and the activity event chain, a composite state vector is generated, and based on a time sequence of the composite state vector, the social relation graph and the activity event chain are subjected to cross-modal fusion processing; and predicting the probability of entering a preset abnormal behavior state (such as social isolation, depression tendency or decline of self-care ability) of the home elderly. According to the invention, the privacy of the user is protected through non-intrusive data acquisition, and the social communication and exclusive activity information of the elderly are fused, so that the transformation from post-event alarm to pre-event early warning is realized, a precious time window is won for guardians or service institutions to take preventive intervention measures, and remarkable social and humanistic values are achieved.