426 results about "Elderly care" patented technology

The invention relates to the technical field of multi-modal data processing, and discloses a multi-modal data fusion processing method and system, and the method comprises the steps: collecting a multi-modal data set containing the activities of the old through an intelligent old-age care robot; constructing a timestamp calibration matrix based on the multi-modal data set, and carrying out dynamic weight correction on the timestamp calibration matrix by adopting a three-modal data envelope delay compensation algorithm to obtain a three-modal data stream with a synchronous time sequence; performing hierarchical feature extraction through a nested feature extractor to obtain a three-mode tumble feature vector group; performing vector fusion on the three-mode tumble feature vector group to obtain tumble fusion feature vectors; and inputting the tumble fusion feature vector into a multi-modal forward prediction model to perform tumble risk trajectory prediction, and outputting a tumble detection result, the method realizes prediction analysis of body posture changes in a key time window before the old man tumble, and improves the ability to distinguish a slow getting-up action and a real tumble behavior of the old man.

The invention relates to the technical field of intelligent robots and old-age care, and discloses a target navigation method and device based on environmental context, a robot and a medium, which can be applied to a household auxiliary robot intelligent medicine delivery scene, and the method comprises the following steps: receiving multi-modal input of a user, preprocessing the multi-modal input, and generating preprocessed multi-modal data; reading the current time and the environment state, and performing perceptual analysis based on a pre-constructed tetrad map, the current time and the environment state to generate a candidate search area list; determining a target candidate area from the candidate search area list; generating a target navigation path based on the SLAM map and the target candidate area, and moving the robot to the target candidate area according to the target navigation path; and obtaining a target candidate region image, and performing visual semantic verification based on the target candidate region image and the target object label in the structured text instruction. According to the invention, the robot navigation accuracy and the user experience are improved.

The invention relates to an emotion accompanying robot system based on multi-dimensional perception and an interaction method, and belongs to the technical field of intelligent robots. The system comprises a sensing module which captures micro-expression, voice, body temperature, touch and other multi-source data through a biological radar array, multispectral imaging and a touch sensing fabric; the decision-making module quantifies the emotion intensity by using an emotion state calculation engine, constructs a user personalized emotion file in combination with the dynamic knowledge graph, and generates a dynamic emotion graph; and the execution module realizes anthropomorphic emotion expression through bionic face driving, joint compliance control and thermal feedback. The interaction method comprises the steps of data capture, emotion file construction, dynamic graph generation, interactive execution and feedback adjustment, definition of an emotion intensity quantification formula, a graph edge weight model and the like. According to the method, physiological signals, environment situations and bionic expression are fused, multi-modal precise emotion perception and safe interaction are achieved, man-machine naturalness is improved, the method is suitable for elderly accompanying, psychological counseling and other scenes, and the emotion accompanying effect is enhanced.

The invention discloses a voice quality inspection method and device, computer equipment and a storage medium, belongs to the technical field of artificial intelligence, and is applied to voice quality inspection scenes in the fields of finance, health medical care, old-age care and the like. The multi-modal feature fusion technology is introduced, the context semantic features of the text and the acoustic features of the voice are extracted, the emotion features are obtained by combining the pre-trained emotion recognition model, comprehensive understanding of the voice data from the three dimensions of semantics, acoustics and emotions is achieved, the deep fusion of the three feature vectors is carried out, and the voice recognition efficiency is improved. Compared with a traditional method which only depends on text or acoustic features, the method has the advantages that multi-modal features are realized by combining emotional features on the basis of the text or acoustic features, and information contained in voice content can be reflected more comprehensively and meticulously, so that the accuracy and practicability of voice quality inspection are improved, and the voice quality inspection efficiency is improved. And the requirements of application scenes such as intelligent customer service and voice auditing on high-quality automatic quality inspection are met.

The invention relates to the technical field of medicine and artificial intelligence, and discloses an elderly care-oriented medical and nursing combined knowledge service system construction method, which comprises the following steps of collecting sentiment expression data of the elderly, applying a nonlinear time sequence analysis and deep learning model, extracting sentiment dynamic characteristics, and obtaining sentiment expression data of the elderly; recognizing an early signal with reduced emotion regulation ability; on the basis of the obtained emotion dynamic features, a memory enhanced attention network is combined with a psychological knowledge graph, an individualized emotion cognitive interaction model of the old people is constructed, a dynamic interaction mechanism between an emotion state and a cognitive mode is revealed, and key intervention nodes are recognized; based on an emotion cognition interaction model and a key node recognition algorithm, the system can accurately position key links of mutual influence of emotion and cognition, and targeted intervention is achieved; and a dual-channel collaborative intervention strategy adjusts an emotional state and reconstructs a cognitive mode through multi-agent reinforcement learning, and breaks through an emotional cognitive vicious circle.

The invention discloses a multi-modal data fusion-based household aging transformation demand evaluation and scheme generation system. According to the system, technologies such as attitude sensing and image acquisition are comprehensively applied, acceleration and angular velocity data of a user are acquired by means of an attitude sensing module, and behavior image data are acquired by an image acquisition module. Fusion analysis is carried out on the multi-mode signals through edge AI processing equipment, accurate recognition of the behavior state of the old people is achieved, and alarm is triggered when an abnormal posture is detected. Meanwhile, the system utilizes the environment sensing module to assist in behavior judgment and path planning, has the advantages of non-intrusive type, high real-time performance, high accuracy and the like, can effectively improve the life safety and behavior monitoring intelligence level of the old people under an unattended condition, is suitable for a safety monitoring scene in a family old people nursing environment, and has a wide application prospect. Good popularization and application values are realized.

The invention discloses a health care service data management method based on machine learning, and relates to the technical field of data management. The method comprises the following steps: collecting health care service data in real time, constructing a health care knowledge graph, and carrying out feature dynamic alignment on the health care knowledge graph by adopting agency attention and multi-scale contrast learning to obtain a dynamic alignment feature vector; based on the dynamic alignment feature vectors, training CatBoost, XGBoost and a random forest model through Bayesian optimization, and obtaining a health prediction model through stacking generalization fusion of a logistic regression model; inputting the dynamic alignment feature vector into a health prediction model, and outputting to obtain a prediction result; corresponding service measures are executed according to the prediction result, new data are collected again after the service measures are executed, the health care knowledge graph is updated, and therefore management of health care service data is achieved.

The invention relates to the technical field of smart home, and discloses a smart home environment regulation and control method and device based on multi-modal behavior perception, and the method and device can understand the home environment parameter preference of a user in different scenes through a home habit reference value. The current physiological state, behavior mode and environment condition of the user can be comprehensively captured by collecting multi-mode sensing data. And calculating a multi-modal data deviation value to quantify the difference between the current home environment and the user habits, and positioning environment factors needing to be adjusted. The multi-modal data deviation value is subjected to weighted fusion calculation according to the weight matrix driven by the current health state of the user, and the comfort degree deviation index is generated, so that the recognition accuracy of user requirements is improved, and personalized and dynamic comfort degree evaluation is realized. An intelligent regulation and control strategy is determined according to the comfort degree deviation index, precise regulation and control of the home environment in the fields of medical science and technology and home-based care for the aged are achieved, and the accuracy and adaptability of home environment parameter regulation and control are effectively improved.

The invention relates to the field of community old-age care, and discloses a community old-age care-oriented medical-care-and-care-combined data intercommunication and service coordination system, which comprises a sensing module used for acquiring physiological data of old people, old-age care service data, environment data and service demand information to form multi-modal information; the network module is used for carrying out multi-modal information transmission, protocol conversion and edge calculation preprocessing to obtain processed data; and the data module is used for constructing a standardized data lake according to the processed data by adopting a unified data standard and a main data management technology, and carrying out fusion storage, treatment and safety protection to obtain multi-source information. Multi-modal information is collected to provide a data basis for cross-mechanism cooperation, data transmission and preprocessing are performed, and a standardized data lake is constructed through a unified data standard and main data management, so that medical institutions, pension institutions and community grids can share global data such as health states, service records and the like of old people in real time.

The invention discloses a robot target navigation method, device and equipment and a medium. The method comprises the following steps: constructing a basic language instruction through a preset language model according to a fuzzy instruction input by a user and a preset family knowledge graph; performing information expansion through the preset language model according to the basic language instruction and preset reference information, and generating a target language instruction; generating a target navigation instruction from the target language instruction and a collected environment image through a preset visual language model; and controlling the robot to move according to the target navigation instruction. The method can be applied to robot navigation scenes in the business fields of medical treatment, health, old-age care and the like. By generating the target navigation instruction to control the robot to move, the problem that an indoor auxiliary robot with a visual language model cannot efficiently provide services for old people can be solved.

The invention discloses a smart old-age service platform based on Internet of Things big data, and belongs to the technical field of Internet of Things old-age care, and the platform specifically comprises a voice instruction collection module which is used for collecting the interaction voice data of an old person and smart home equipment in real time; the voice feature extraction module is used for carrying out feature separation processing on the collected original voice data and extracting quantized instruction indexes; the baseline model construction module is used for establishing a voice health baseline model based on user characteristics and generating a personalized voice index reference interval according to the historical voice data of the old; the voice instruction comparison module is used for dynamically comparing the real-time instruction index with the baseline model, generating a voice health deviation degree curve, and triggering early warning when curve fluctuation exceeds a preset morphological characteristic; the interaction mode adjusting module is used for adjusting the interaction mode of the smart home equipment according to the voice health deviation degree; the health of the elderly is judged through the voice index change, and the smart home service quality is improved.

The invention relates to the technical field of intelligent control, in particular to an internet-of-things-based old-age care robot intelligent control system, which comprises a multi-mode sensing layer, a safety decision center, an execution driving module, a voice interaction optimization module and an internet-of-things communication module. According to the method, force sense data such as contact force of the mechanical arm, voice data such as voices of old people and view data such as obstacles are collected in real time through a multi-mode sensing layer, a displacement force coupling risk value is calculated through a hyperbolic tangent function coupling model of a safety decision center, and a deviation angle risk value is analyzed and calculated through an improved Lyapunov index spectrum; a total risk value is generated through fusion and is scheduled according to hierarchical intervals, mechanical operation, old people behaviors and environmental risks can be dynamically avoided, and the use safety is remarkably improved; through sub-ambiguity quantitative analysis, comprehensive ambiguity evaluation and intention correction of the voice interaction optimization module, instruction intention is optimized in combination with multi-modal data, the problem of fuzzy voice recognition of old people is effectively solved, and interaction accuracy and response efficiency are greatly improved.

The invention relates to the technical field of old-age care monitoring, and discloses an intelligent old-age care monitoring method and system based on wearable equipment. According to the method, original physiological data such as heart rate signals, oxyhemoglobin saturation and body movement frequency are collected through a plurality of wearable devices, multi-modal feature extraction is conducted on the original physiological data, and a physiological feature vector set is generated. A to-be-analyzed health event sequence containing a periodic abnormal mode and a non-periodic burst mode is screened out, and a health knowledge base is called to retrieve a matched candidate health event entry set. And inputting the two health events into a health event aggregator for cross-modal semantic fusion analysis, generating a health event decision coding matrix, obtaining an intervention measure instruction set for each health event to be analyzed through hierarchical decoding, and finally generating a response operation queue executable by the pension monitoring terminal. According to the method, the health condition of the old people can be comprehensively and accurately monitored, intervention measures can be timely and effectively provided, and the quality and efficiency of old-age care monitoring are greatly improved.

The invention discloses a health monitoring system of an old-age care robot, which relates to the technical field of health monitoring, and comprises the steps of constructing an indoor multi-modal data pool, generating an encrypted feature vector, establishing a steady-state physiological behavior baseline according to a historical encrypted feature vector, and outputting a health risk level and an intervention instruction set. According to the invention, all-time and all-scene health monitoring is realized by integrating a multi-mode sensor, old people do not need to wear equipment, feature compression and homomorphic encryption technologies are adopted, data security is ensured, a threshold value can be updated in real time, and the method is suitable for being applied to the health monitoring of the old people. The health state of the old people is accurately monitored, health risk prediction and personalized intervention are carried out in combination with analysis of behaviors such as falling and static abnormity, the prompt mode is dynamically adjusted according to the ability of the old people, the data processing efficiency is optimized through the edge cloud collaborative architecture, the data privacy and stability are guaranteed, and the life quality and safety are improved.

The invention provides a time bank system for intelligent old-age care service, a volunteer scheduling method, a storage medium and electronic equipment, and relates to the technical field of Internet. The method comprises the steps of presetting coordinate parameters of an electronic geo-fence of a target service area, and generating corresponding access event data when a mobile terminal enters or leaves the electronic geo-fence; determining service start time and service end time of each wisher based on the in-out event data, and generating corresponding service duration data according to the service start time and the service end time; the feature extraction module is used for acquiring static information and dynamic service data related to candidate volunteers from a database, performing feature extraction processing on the information in combination with the service duration data, and generating feature vectors for matching; and calculating a matching score between the candidate volunteer and the target service object based on the feature vector and a preset machine learning model, and generating matching relation data based on the matching score.

The invention belongs to the technical field of artificial intelligence, and discloses an old-age service product recommendation method and device, equipment and a storage medium, and the method comprises the steps: receiving client house data, constructing a virtual old-age service space according to the client house data, and marking a high-risk region in the virtual old-age service space; receiving an interaction instruction, demonstrating a virtual risk scene in a high-risk area of the virtual pension space according to the interaction instruction, collecting behavior actions of a user during demonstration of the virtual risk scene, and generating a first interest value according to the behavior actions; recording and calculating a second interest value according to the residence time and the access frequency of the user in each high-risk area in the virtual pension space; based on each high-risk area, performing weighted summation on the first interest value and the second interest value to obtain a target interest value, determining a target recommended product according to the target interest value, and recommending the target old-age service product to the user; the method can be applied to an implementation scene of product recommendation in the financial field, and the customer conversion rate is improved.

The invention provides an elderly nursing method and system, which is applied to elderly nursing services, and comprises the following steps: receiving a video link generated when a monitoring camera monitors an identification object through a message push service; acquiring user configuration information from a database; sending the video link and the user configuration information to an artificial intelligence cloud service, so that the artificial intelligence cloud service analyzes the dangerous behavior and the daily behavior of the identified object to obtain an analysis result; an analysis result sent by the artificial intelligence cloud service is received, and daily state information of the recognition object is counted according to the analysis result; comparing the daily state information with the user configuration information to obtain a comparison result; through combination of the intelligent camera and AI behavior analysis, abnormal or high-risk behaviors of the elderly can be fed back in real time and pushed to family members in time for first-time processing, so that the family members can comprehensively master the living state of the elderly, potential health risks or living abnormalities can be found in time, and the user experience is improved.

The invention relates to the technical field of image processing, can be applied to the fields of old-age care and the like, and discloses an unsupervised abnormal behavior detection method, device and equipment and a storage medium, and the method comprises the steps: collecting video data, and carrying out the frame extraction processing of the video data, and obtaining a previous frame of a current frame, the current frame and a next frame of the current frame; inputting a previous frame of the current frame and a next frame of the current frame into an optical flow estimation network and an interpolation network to generate a prediction frame; inputting the current frame and the prediction frame into a feature extraction network to respectively obtain a current feature and a prediction feature; calculating a loss function value according to the current frame, the prediction frame, the current feature and the prediction feature; judging whether the loss function value reaches a preset threshold value; and if the loss function value does not reach the preset threshold value, determining that the behavior is abnormal. According to the invention, through the optical flow estimation network and the interpolation network, whether others show abnormal motion trails can be effectively identified, so that the accuracy and robustness of abnormal behavior detection are improved.

The invention relates to the technical field of articles for old people, in particular to an adjusting method, system, equipment and medium for improving the short circuit ratio of a new energy station, and the method comprises the steps: calculating a first target index and a second target index of the new energy station, and switching a part of grid-connected converters into a virtual synchronous generator control mode; when the second target index does not meet the short-circuit ratio control requirement, changing the equivalent impedance of the grid-connected point by adjusting the virtual impedance of the virtual synchronous generator so as to reduce the second target index of the new energy station; and when the grid fault causes the voltage drop of the grid-connected point, reactive voltage control is carried out through the virtual exciter of the virtual synchronous generator, and voltage support is provided for the grid-connected point. The method has the beneficial effects that flexible and efficient dynamic adjustment is realized by using the virtual synchronous generator, the system operation efficiency is improved, and the short-circuit ratio of a new energy field station is improved by combining virtual impedance and a reactive power support function, so that the operation capability of the new energy field station in a weak power grid is enhanced.

The invention provides an old-age care robot dialogue method and system based on a large language model in the technical field of service robots, and the method comprises the steps: S1, enabling an old-age care robot to obtain input user voice data, and carrying out the preprocessing of the user voice data; s2, identifying the preprocessed user voice data to obtain user text data; s3, inputting the user text data into a pre-deployed dialogue model to generate a local feedback text, calculating a semantic matching degree between the user text data and the local feedback text through a pre-deployed semantic matching model, and uploading the user text data to a large language model deployed by a server based on the semantic matching degree to obtain an online feedback text; and S4, converting the local feedback text or the online feedback text into feedback voice data through a voice synthesis technology, and playing the feedback voice data. The system has the advantage that the man-machine interaction capability and expansibility of the old-age care robot are greatly improved.

The invention relates to the technical field of text generation, can be applied to the fields of financial science and technology and medical health care services, and discloses a text generation method and device based on semantic anchor points, equipment and a medium, and the method comprises the steps: obtaining an original video, and analyzing the original video to obtain an original video frame sequence; semantic anchor point extraction is carried out on the original video frame sequence to obtain a plurality of semantic anchor points, and one semantic anchor point corresponds to one core event in the original video; performing causal constraint on the plurality of semantic anchor points to obtain a semantic anchor point sequence; and generating text information for describing the original video based on the original video frame sequence and the semantic anchor point sequence. It can be ensured that the generated text information has context continuity.

The present invention relates to the technical field of aerosol drug delivery, and discloses a cavity aerosol drug delivery device for elderly care, comprising a main body, a detachable aerosol canister disposed inside the main body, a spray valve for inhaling drugs from the aerosol canister disposed on the upper side of the main body, a main cover fixedly disposed on the left side of the spray valve, and a fixed cover fixedly mounted on the left side of the main cover. The cavity aerosol drug delivery device for elderly care, by respectively placing two support parts against the upper and lower gums of the patient's mouth, and using a support rod to drive the support parts to move relative to each other, can achieve the purpose of opening the elderly patient's mouth, which is beneficial for nursing staff to administer drug treatment to the elderly patient, solves the problem that the elderly patient's mouth is difficult to open on its own, resulting in low drug delivery efficiency, further improves the treatment effect on the elderly patient, and is beneficial for the elderly patient to recover as soon as possible.

The invention discloses a meal-assisting management system and a meal-assisting machine for old-age service, and relates to the technical field of dining management for old-age service, the meal-assisting management system and the meal-assisting machine for old-age service are characterized in that physiological parameters of old people are acquired in real time through a health data acquisition module, and a taboo food material list is generated according to the physiological parameters and the taboo food material list through a nutrition matching engine; the meal dispatching server generates a meal preparation instruction based on the meal preparation instruction, and personalized meal services conforming to the health conditions of the old people can be provided for the old people; a data desensitization unit of the health data acquisition module encrypts identity fields in physiological parameters, and establishes secure connection with a medical insurance database based on an SM2 national cryptographic algorithm bidirectional authentication protocol to ensure the security and privacy of data of the old; a space-time prediction unit of the meal dispatching server adopts a convolutional neural network to process historical meal records, and the meal delivery amount in different periods can be accurately predicted; and the path optimization unit calculates an optimal distribution path based on a genetic algorithm, so that the distribution efficiency is improved, and the distribution cost is reduced.

The invention discloses an intelligent community health care monitoring system based on cloud computing, relates to the technical field of intelligent community health monitoring, and aims to solve the problems of difficulty in multi-source heterogeneous data fusion, insufficient real-time performance and remarkable service response delay. The system comprises a multi-modal data acquisition terminal, a data fusion and preprocessing gateway, a core intelligent analysis engine and an interactive response terminal, and efficient and accurate health monitoring and early warning are realized through standardized fusion of multi-source data and behavior pattern recognition and hierarchical risk assessment based on deep learning. And the pension service response efficiency and reliability are obviously improved.

The invention discloses a foggy day self-adaptive pedestrian trajectory prediction method for an old-age care robot. The method comprises the following steps: S1, extracting foggy day physical parameters and fog invariant features; s2, based on a multi-scale Mama space-time sequence module, extracting fusion features in the foggy weather; s3, constructing a fog sensing dynamic heterogeneous network; and S4, trajectory modeling is carried out by using the fusion features. The invention also discloses a foggy day self-adaptive pedestrian trajectory prediction system for the old-age care robot, which is applied to the foggy day self-adaptive pedestrian trajectory prediction method for the old-age care robot. Comprising a physical prior fusion fog sign decoupling unit used for estimating foggy day physical parameters and extracting fog invariant features, and a multi-scale Mamba space-time sequence module used for extracting space-time fusion features in a foggy day environment. The fog perception dynamic heterogeneous network is used for modeling the social relation of pedestrians in foggy days and fusing the spatio-temporal features; and the trajectory generation module is used for generating a prediction trajectory based on the fused features.

The invention relates to an aging-suitable transformation recommendation method and system, electronic equipment and a storage medium, and relates to the technical field of smart pension, and the transformation recommendation method comprises the steps: obtaining the temperature and humidity in a living room and a living room state diagram, and collecting the appearance form, the placement position, the home use frequency and the home use duration of home articles displayed in the living room; judging the home damage level by adopting a home full-life monitoring model; acquiring physiological index data of the supervised object, and generating a home life table; according to the household damage level and the living room state diagram, generating aging-suitable transformation reminding information, determining household article transformation requirements of the supervision object in combination with a household life table, and sending the household article transformation requirements to the supervision subject; generating a household position adjustment graph based on the household article transformation demand and the placement position; generating a novel home recommendation table according to the home article transformation demand and the appearance form in combination with the home market data; the living safety and life quality of the supervised object are improved, and the evaluation effectiveness of the aging degree of the household articles is improved.

The invention discloses an elder dysphagia alarm system based on a multi-modal artificial intelligence model. The elder dysphagia alarm system comprises a data acquisition module, a multi-modal fusion module, an artificial intelligence analysis module, an alarm module and a background management system. The data acquisition module acquires video image data, wearable equipment data and environment data of the elderly; the multi-modal fusion module carries out fusion processing on the collected data of different modals to generate a multi-modal feature vector; the artificial intelligence analysis module analyzes the feature vectors based on a multi-modal artificial intelligence model, and outputs the probability and confidence of dysphagia of the old; when the elder is dysphagia and the confidence reaches a preset threshold, the alarm module starts to give an alarm; and the background management system configures, manages and monitors the system. The dysphagia condition of the old people is efficiently and accurately detected and timely alarmed by fusing various modal data and utilizing a deep learning technology, a powerful guarantee is provided for the safety and health of the old people, and the dysphagia condition detection method is suitable for the scenes of old people institutions, community old people service centers, families of the old people living alone and the like.

The invention relates to the technical field of smart old-age care, and discloses a personalized information rehabilitation task generation method, which comprises the following steps: acquiring rehabilitation data of old people in a nursing home and interaction data of visitors to form basic data; based on the rehabilitation data and the interaction data, constructing comprehensive portraits of the old and the visitors; comprehensive features of the comprehensive portrait are extracted, and initial task matching is carried out in a rehabilitation task library; performing multi-target weighted calculation on the initial tasks based on the old and visitors to obtain matching degrees corresponding to the initial tasks, and selecting three tasks from high to low according to the matching degrees as selection tasks; the senior side and the visitor side perform satisfaction scoring on the selected task, and the task with the highest comprehensive satisfaction is selected as a final rehabilitation task; the optimal rehabilitation task is generated through multi-target weighted calculation and a bidirectional scoring mechanism, dynamic accurate matching and safety intensity control are achieved, and the method has the advantage that the matching accuracy and safety of the rehabilitation task are improved.

The invention relates to the technical field of computers, and discloses a service node processing method, device and equipment and a medium, which are applied to a service node scheduling system and comprise a scheduling module, a calculation module, a processing module and a state management module. Obtaining an effective weight and a current weight of each service node in the current service cluster; calculating a target scheduling priority value of the service node based on the current weight and the effective weight through a calculation module; determining the service node with the highest target scheduling priority value as a target service node through a processing module, and processing the target service node to obtain a processing result; and if the state management module determines that the processing result is in a successful state, the effective weight of the target service node is increased. The method can be applied to business management program systems such as financial science and technology, medical health, old-age care and the like, and the accuracy of service node request distribution and the stability of system operation can be improved.

The application belongs to the field of medical health, and provides a home-based care service matching processing method and device based on machine learning and related equipment, in order to solve the problem of low matching efficiency of home-based care services in traditional technologies, by data fusion of health status information data, self-care ability information data, living state information data and mental health information data in different dimensions, determining the user portrait and customer category corresponding to the home-based care customer according to the fused data, then based on the preset prediction model of machine learning, and according to the user portrait and customer category, predicting the home-based care service demand corresponding to the home-based care customer, finally determining the target home-based care service project corresponding to the home-based care service demand, and matching the target home-based care service project to the home-based care customer, realizing the improvement of the accuracy of home-based care service matching according to the multi-dimensional information of the home-based care customer and based on the prediction of machine learning.